diff --git a/ModuleSpec.chpl b/ModuleSpec.chpl
index d5683e0d7..d73e4a90b 100644
--- a/ModuleSpec.chpl
+++ b/ModuleSpec.chpl
@@ -6,20 +6,20 @@ import Time;
 
 
 // Construct the model from specification. 
-var m: owned Module(real) = modelFromSpecFile("scripts/models/cnn/specification.json");
+var model: owned Module(real) = modelFromSpecFile("scripts/models/cnn/specification.json");
 
 // Print the model's structure. 
-writeln(m.signature);
+writeln(model.signature);
 
 // Load the weights into the model. 
-m.loadPyTorchDump("scripts/models/cnn/");
+model.loadPyTorchDump("scripts/models/cnn/");
 
 // Load an array of images. 
-config const imageCount = 1;
-var images = forall i in 0..<imageCount do Tensor.load("data/datasets/mnist/image_idx_" + i:string + ".chdata");
+config const numImages = 1;
+var images = forall i in 0..<numImages do Tensor.load("data/datasets/mnist/image_idx_" + i:string + ".chdata");
 
 // Create array of output results. 
-var preds: [images.domain] int;
+var preds: [0..<numImages] int;
 
 
 
@@ -27,17 +27,20 @@ config const numTimes = 1;
 var time: real;
 for i in 0..<numTimes {
     var st = new Time.stopwatch();
+
     st.start();
     forall (img,pred) in zip(images, preds) {
-        var output: Tensor(real) = m(img);
-        pred = output.argmax();
+        pred = model(img).argmax();
     }
-    const tm = st.elapsed();
     st.stop();
+
+    const tm = st.elapsed();
     writeln("Time: ", tm, " seconds.");
-    time = tm;
+    time += tm;
 }
 
+time /= numTimes;
+
 config const printResults = false;
 if printResults {
     for i in images.domain {
@@ -45,4 +48,4 @@ if printResults {
     }
 }
 
-writeln("The last inference batch took ", time, " ms.");
\ No newline at end of file
+writeln("The average inference time for batch of size ", numImages, " was ", time, " seconds.");
\ No newline at end of file
diff --git a/docs/.buildinfo b/docs/.buildinfo
new file mode 100644
index 000000000..94ab99e95
--- /dev/null
+++ b/docs/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 88f41822c6ec599496c47c238ba72239
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
new file mode 100644
index 000000000..691977093
--- /dev/null
+++ b/docs/_sources/index.rst.txt
@@ -0,0 +1,23 @@
+chpldoc documentation
+=====================
+
+Contents:
+
+.. toctree::
+   :hidden:
+
+   self
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   modules/**
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :chpl:chplref:`chplmodindex`
+* :ref:`search`
diff --git a/docs/_sources/modules/lib/Autograd.rst.txt b/docs/_sources/modules/lib/Autograd.rst.txt
new file mode 100644
index 000000000..969387482
--- /dev/null
+++ b/docs/_sources/modules/lib/Autograd.rst.txt
@@ -0,0 +1,329 @@
+.. default-domain:: chpl
+
+.. module:: Autograd
+
+Autograd
+========
+**Usage**
+
+.. code-block:: chapel
+
+   use Autograd;
+
+
+or
+
+.. code-block:: chapel
+
+   import Autograd;
+
+.. function:: proc checkRank(te: shared TensorEssence(?eltType), param rank: int): bool
+
+.. function:: proc getRank(te: shared TensorEssence(?eltType)): int
+
+.. function:: proc forceRank(te: shared TensorEssence(?eltType), param rank: int): shared BaseTensorResource(eltType, rank)
+
+.. class:: TensorEssence : serializable
+
+   .. attribute:: type eltType = real
+
+   .. method:: proc runtimeRank: int
+
+.. class:: ForgetfulTensor : TensorEssence
+
+   .. attribute:: param rank: int
+
+.. class:: BaseTensorResource : TensorEssence, serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: var dataResource: remote(ndarray(rank, eltType))
+
+   .. attribute:: var gradResource: remote(ndarray(rank, eltType))
+
+   .. method:: proc to(device_: locale)
+
+   .. method:: proc device: locale
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad: remote(ndarray(rank, eltType)), param alreadyPopulated = false)
+
+   .. method:: proc backward() where rank == 1
+
+   .. method:: proc array ref: ndarray(rank, eltType)
+
+   .. method:: proc data ref: [] eltType
+
+   .. method:: proc grad ref: ndarray(rank, eltType)
+
+   .. method:: proc gradData ref: ndarray(rank, eltType)
+
+   .. method:: proc _loc
+
+   .. method:: override proc runtimeRank: int
+
+.. class:: TensorResource : BaseTensorResource(?), serializable
+
+   .. attribute:: type operation
+
+   .. attribute:: var operationData: operation
+
+   .. method:: proc init(param rank: int, type eltType, type operation)
+
+   .. method:: proc init(param rank: int, type eltType, operationData: ?operation)
+
+   .. method:: proc init(ref resource: remote(ndarray(?rank, ?eltType)), operationData: ?operation, device_: locale = defaultDevice)
+
+   .. method:: proc init(data: ndarray(?rank, ?eltType), operationData: ?operation, device_: locale = defaultDevice)
+
+   .. method:: override proc forward()
+
+   .. method:: override proc backward(grad: remote(ndarray(rank, eltType)), param alreadyPopulated: bool = false)
+
+   .. method:: override proc backward() where rank == 1
+
+.. record:: baseValue : serializable
+
+   .. method:: proc forward()
+
+   .. method:: proc children
+
+.. record:: reluOp : serializable
+
+   .. attribute:: var input: shared BaseTensorResource(?)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+   .. method:: proc _relu(x)
+
+.. record:: expOp : serializable
+
+   .. attribute:: var input: shared BaseTensorResource(?)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+.. record:: addOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType
+
+   .. attribute:: var lhs: shared BaseTensorResource(eltType, rank)
+
+   .. attribute:: var rhs: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(rank, eltType)
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType), ndarray(rank, eltType))
+
+.. record:: subOp : serializable
+
+   .. attribute:: var lhs: shared BaseTensorResource(?)
+
+   .. attribute:: var rhs: shared BaseTensorResource(?)
+
+   .. method:: proc forward()
+
+.. record:: divOp : serializable
+
+   .. attribute:: var lhs: shared BaseTensorResource(?)
+
+   .. attribute:: var rhs: shared BaseTensorResource(?)
+
+   .. method:: proc forward()
+
+.. record:: multOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType
+
+   .. attribute:: var lhs: shared BaseTensorResource(eltType, rank)
+
+   .. attribute:: var rhs: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType), ndarray(rank, eltType))
+
+.. record:: reshapeOp : serializable
+
+   .. attribute:: param oldRank: int
+
+   .. attribute:: param newRank: int
+
+   .. attribute:: type eltType
+
+   .. attribute:: var shape: newRank*int
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, oldRank)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(newRank, eltType)
+
+   .. method:: proc backward(grad: ndarray(newRank, eltType)): (ndarray(oldRank, eltType),)
+
+.. record:: permuteOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var permutation
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: expandOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var expandedShape: rank*int
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: padOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var arg: rank*(2*(int))
+
+   .. attribute:: var value: eltType
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(rank, eltType)
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: shrinkOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var arg: rank*(2*(int))
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(rank, eltType)
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: sliceOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var dom: domain(rank, int)
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(rank, eltType)
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: layerSliceOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var base: shared BaseTensorResource(eltType, rank)
+
+   .. attribute:: var mask: shared BaseTensorResource(eltType, rank)
+
+   .. attribute:: var maskDomain: domain(rank, int)
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(rank, eltType)
+
+   .. method:: proc backward(grad: ndarray(rank, eltType)): (ndarray(rank, eltType), ndarray(rank, eltType))
+
+.. record:: sumOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: param sumRank: int
+
+   .. attribute:: var axes: sumRank*int
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc outRank param: int
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad: ndarray(outRank, eltType)): (ndarray(rank, eltType),)
+
+.. record:: maxOp : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: param maxRank: int
+
+   .. attribute:: var axes: maxRank*int
+
+   .. attribute:: var input: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc children
+
+   .. method:: proc forward()
+
+   .. method:: proc backward(grad): (ndarray(rank, eltType),)
+
+.. record:: conv2DOp : serializable
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var features: shared BaseTensorResource(eltType, 3)
+
+   .. attribute:: var kernel: shared BaseTensorResource(eltType, 4)
+
+   .. attribute:: var stride: int
+
+   .. method:: proc children
+
+   .. method:: proc forward(): ndarray(3, eltType)
+
+   .. method:: proc backward(grad: ndarray(3, eltType)): (ndarray(3, eltType), ndarray(4, eltType))
+
diff --git a/docs/_sources/modules/lib/DynamicTensor.rst.txt b/docs/_sources/modules/lib/DynamicTensor.rst.txt
new file mode 100644
index 000000000..e85eb0ba2
--- /dev/null
+++ b/docs/_sources/modules/lib/DynamicTensor.rst.txt
@@ -0,0 +1,137 @@
+.. default-domain:: chpl
+
+.. module:: DynamicTensor
+
+DynamicTensor
+=============
+**Usage**
+
+.. code-block:: chapel
+
+   use DynamicTensor;
+
+
+or
+
+.. code-block:: chapel
+
+   import DynamicTensor;
+
+.. data:: config param maxRank = 6
+
+.. record:: Tensor : serializable
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var meta: shared TensorEssence(eltType)
+
+   .. attribute:: var runtimeRank: int = -1
+
+   .. method:: proc init(type eltType)
+
+   .. method:: proc init(type eltType, meta: shared TensorEssence(eltType))
+
+   .. method:: proc init(meta: shared TensorEssence(?eltType))
+
+   .. method:: proc init(t: tensor(?rank, ?eltType))
+
+   .. method:: proc init(a: ndarray(?rank, ?eltType))
+
+   .. method:: proc init(arr: [] ?eltType)
+
+   .. method:: proc this(args ...)
+
+   .. method:: proc tensorize(param rank: int): tensor(rank, eltType)
+
+   .. method:: proc resource(param rank: int): shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc forceRank(param rank: int): tensor(rank, eltType)
+
+   .. method:: proc forceRankMeta(param rank: int): shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc hardCheckRank(param rank: int): bool
+
+   .. method:: proc checkRank(param rank: int): bool
+
+   .. method:: proc to(device: locale)
+
+   .. method:: proc device: locale
+
+   .. method:: proc array(param rank: int) ref: ndarray(rank, eltType)
+
+   .. method:: proc grad(param rank: int) ref: ndarray(rank, eltType)
+
+   .. method:: proc data(param rank: int) ref: [] eltType
+
+   .. method:: proc toNDArray(param rank: int): ndarray(rank, eltType)
+
+   .. method:: proc toArray(param rank: int): [] eltType
+
+.. method:: proc ndarray.toTensor(): Tensor(eltType)
+
+.. method:: proc tensor.eraseRank(): Tensor(eltType)
+
+.. function:: operator :(t: tensor(?rank, ?eltType), type T: Tensor(eltType)): Tensor(eltType)
+
+.. function:: proc zipBinOp(param opName: string, a: Tensor(?eltType), b: Tensor(eltType)): Tensor(eltType)
+
+.. method:: proc type Tensor.loadFromNumpy(path: string): Tensor(real)
+
+.. function:: operator +(a: Tensor(?eltType), b: Tensor(eltType)): Tensor(eltType)
+
+.. function:: operator -(a: Tensor(?eltType), b: Tensor(eltType)): Tensor(eltType)
+
+.. function:: operator *(a: Tensor(?eltType), b: Tensor(eltType)): Tensor(eltType)
+
+.. method:: proc Tensor.sum(axes: int ...?r): Tensor(eltType)
+
+.. method:: proc Tensor.relu(): Tensor(eltType)
+
+.. method:: proc Tensor.max(): Tensor(eltType)
+
+.. method:: proc Tensor.exp(): Tensor(eltType)
+
+.. method:: proc Tensor.softmax(): Tensor(eltType)
+
+.. method:: proc Tensor.maxPool(poolSize: int): Tensor(eltType)
+
+.. method:: proc Tensor.reshape(args ...): Tensor(eltType)
+
+.. method:: proc Tensor.slice(rngs: range ...?rank): Tensor(eltType)
+
+.. method:: proc Tensor.slice(dom: domain(?)): Tensor(eltType)
+
+.. method:: proc Tensor.flatten(): Tensor(eltType)
+
+.. method:: proc type Tensor.matvecmul(m: Tensor(?eltType), v: Tensor(eltType)): Tensor(eltType)
+
+.. method:: proc type Tensor.matvecmulFast(m: Tensor(?eltType), v: Tensor(eltType)): Tensor(eltType)
+
+.. method:: proc Tensor.argmax(): int
+
+.. method:: proc type Tensor.convolve(features: Tensor(?eltType), kernel: Tensor(eltType), stride: int): Tensor(eltType)
+
+.. method:: proc type Tensor.convolve(features: Tensor(?eltType), kernel: Tensor(eltType), bias: Tensor(eltType), stride: int): Tensor(eltType)
+
+.. method:: proc type Tensor.arange(args ...)
+
+.. method:: proc type Tensor.ones(args ...)
+
+.. method:: proc type Tensor.zeros(args ...)
+
+.. function:: proc main()
+
+.. method:: proc Tensor.serialize(writer: IO.fileWriter(locking = false, IO.defaultSerializer), ref serializer: IO.defaultSerializer)
+
+.. method:: proc Tensor.serialize(writer: IO.fileWriter(?), ref serializer: ?srt2) where srt2 != IO.defaultSerializer
+
+.. method:: proc Tensor.write(fw: IO.fileWriter(?)) throws
+
+.. method:: proc Tensor.save(path: string)
+
+.. method:: proc type Tensor.multiReader(path: string)
+
+.. method:: proc type Tensor.load(path: string, param precision = 64): Tensor(real)
+
+.. method:: proc type Tensor.readInPlace(fr: IO.fileReader(?), param precision = 64): Tensor(real)
+
diff --git a/docs/_sources/modules/lib/LoadNumpy.rst.txt b/docs/_sources/modules/lib/LoadNumpy.rst.txt
new file mode 100644
index 000000000..27a6730f6
--- /dev/null
+++ b/docs/_sources/modules/lib/LoadNumpy.rst.txt
@@ -0,0 +1,37 @@
+.. default-domain:: chpl
+
+.. module:: LoadNumpy
+
+LoadNumpy
+=========
+**Usage**
+
+.. code-block:: chapel
+
+   use LoadNumpy;
+
+
+or
+
+.. code-block:: chapel
+
+   import LoadNumpy;
+
+.. class:: NoRankArray
+
+.. class:: ArrClass : NoRankArray
+
+   .. attribute:: param rank: int
+
+   .. attribute:: var _domain: domain(rank, int)
+
+   .. attribute:: var data: [_domain] real
+
+.. function:: proc readData(param rank: int, fr, shape: rank*int, dtype: bytes): owned NoRankArray
+
+.. function:: proc readShape(fr)
+
+.. function:: proc loadNumpyArray(path: string): owned NoRankArray
+
+.. function:: proc main()
+
diff --git a/docs/_sources/modules/lib/NDArray.rst.txt b/docs/_sources/modules/lib/NDArray.rst.txt
new file mode 100644
index 000000000..ccf782d99
--- /dev/null
+++ b/docs/_sources/modules/lib/NDArray.rst.txt
@@ -0,0 +1,205 @@
+.. default-domain:: chpl
+
+.. module:: NDArray
+
+NDArray
+=======
+**Usage**
+
+.. code-block:: chapel
+
+   use NDArray;
+
+
+or
+
+.. code-block:: chapel
+
+   import NDArray;
+
+.. class:: NDArrayData : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real(64)
+
+   .. attribute:: var _domain: domain(rank, int) = util.emptyDomain(rank)
+
+   .. attribute:: var data: [_domain] eltType = noinit
+
+   .. method:: proc init(param rank: int, type eltType)
+
+   .. method:: proc init(param rank: int, type eltType, dom: domain(rank, int))
+
+   .. method:: proc init(param rank: int, type eltType, dom: domain(rank, int), A: [dom] eltType)
+
+   .. method:: proc init(A: [] ?eltType)
+
+   .. method:: proc init(me: NDArrayData(?rank, ?eltType))
+
+.. record:: ndarray : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real(64)
+
+   .. attribute:: var arrayResource: owned NDArrayData(rank, eltType)
+
+   .. method:: proc borrowResource(): borrowed NDArrayData(rank, eltType)
+
+   .. method:: proc resource: borrowed NDArrayData(rank, eltType)
+
+   .. method:: proc data ref: arrayResource.data.type
+
+   .. method:: proc init(param rank: int, type eltType = real(64))
+
+   .. method:: proc init(dom: ?t, type eltType = real(64)) where isDomainType(t)
+
+   .. method:: proc init(type eltType = real(64), shape: int ...?rank)
+
+   .. method:: proc init(arr: [] ?eltType, param isNormal: bool) where isNormal == true
+
+   .. method:: proc init(arr: [] ?eltType, param isNormal: bool) where isNormal == false
+
+   .. method:: proc init(arr: [] ?eltType)
+
+   .. method:: proc init(A: ndarray(?rank, ?eltType))
+
+   .. method:: proc init(it: _iteratorRecord)
+
+   .. method:: proc init=(other: [] ?eltType)
+
+   .. method:: proc init=(other: ndarray(?rank, ?eltType))
+
+   .. method:: proc init=(other: _iteratorRecord)
+
+   .. method:: proc ref this(args: int ...rank) ref
+
+   .. method:: proc ref setData(arr: [] eltType) where arr.rank == rank
+
+   .. method:: proc ref reshapeDomain(dom: arrayResource._domain.type)
+
+   .. method:: proc reshape(dom: arrayResource._domain.type): ndarray(rank, eltType)
+
+   .. method:: proc reshape(dom: ?t): ndarray(dom.rank, eltType) where isDomainType(t) && dom.rank != rank
+
+   .. method:: proc reshape(newShape: int ...?newRank): ndarray(newRank, eltType)
+
+   .. method:: proc slice(args ...)
+
+   .. method:: proc permute(axes: int ...rank)
+
+   .. method:: proc expand(axes: int ...rank)
+
+   .. method:: proc ref sumOneAxis(axis: int): ndarray(rank, eltType)
+
+   .. method:: proc sumAxesMask(withAxesMask: rank*int): ndarray(rank, eltType)
+
+   .. method:: proc sum(axes: int ...?axesCount): ndarray(rank, eltType)
+
+   .. method:: proc shrink(narg: 2*int ...rank, param exactBounds = false): ndarray(rank, eltType)
+
+   .. method:: proc pad(narg: 2*int ...rank, value: eltType = 0): ndarray(rank, eltType)
+
+   .. method:: proc dilate(dil: int) where rank == 2
+
+   .. method:: proc dilate(dil: int) where rank == 3
+
+   .. method:: proc squeeze(param newRank: int): ndarray(newRank, eltType) where newRank < rank
+
+   .. method:: proc max(): ndarray(1, eltType)
+
+   .. method:: proc max(axes: int ...?axesCount): ndarray(rank, eltType)
+
+   .. method:: proc populateRemote(ref re: remote(ndarray(rank, eltType))): remote(ndarray(rank, eltType))
+
+   .. method:: proc toRemote(): remote(ndarray(rank, eltType))
+
+   .. itermethod:: iter ref batchify(param dim: int = 0) ref where dim < rank
+
+   .. method:: proc kernelRot(): ndarray(4, eltType) where rank == 4
+
+   .. method:: proc kernelRot(): ndarray(3, eltType) where rank == 3
+
+   .. method:: proc argmax() where rank == 1
+
+   .. method:: proc relu()
+
+.. method:: proc type ndarray.arange(to: int, type eltType = real(64), shape: ?rank*int): ndarray(rank, eltType)
+
+.. function:: operator  = (ref lhs: ndarray(?rank, ?eltType), rhs: ndarray(rank, eltType))
+
+.. function:: operator  = (ref lhs: ndarray(?rank, ?eltType), rhs: [?d] eltType) where d.rank == rank
+
+.. function:: operator :(val: [] ?eltType, type t: ndarray(val.rank, eltType))
+
+.. method:: proc remote.init(other: ndarray(?rank, ?eltType))
+
+.. method:: proc remote.init=(ref other: ndarray(?rank, ?eltType))
+
+.. function:: operator  = (ref lhs: remote(ndarray(?rank, ?eltType)), rhs: ndarray(rank, eltType))
+
+.. function:: operator :(val: ndarray(?rank, ?eltType), type t: remote(ndarray(rank, eltType)))
+
+.. method:: proc remote.init(ref other: remote(ndarray(?rank, ?eltType)))
+
+.. method:: proc remote.init=(ref other: remote(ndarray(?rank, ?eltType)))
+
+.. function:: operator  = (ref lhs: remote(ndarray(?rank, ?eltType)), rhs: remote(ndarray(rank, eltType)))
+
+.. function:: proc zipArr(a: ndarray(?rank, ?eltType), b: ndarray(rank, eltType), f): ndarray(rank, eltType)
+
+.. function:: operator +(a: ndarray(?rank, ?eltType), b: ndarray(rank, eltType)): ndarray(rank, eltType)
+
+.. function:: operator *(a: ndarray(?rank, ?eltType), b: ndarray(rank, eltType)): ndarray(rank, eltType)
+
+.. function:: operator -(a: ndarray(?rank, ?eltType), b: ndarray(rank, eltType)): ndarray(rank, eltType)
+
+.. function:: operator /(a: ndarray(?rank, ?eltType), b: ndarray(rank, eltType)): ndarray(rank, eltType)
+
+.. function:: operator +(a: remote(ndarray(?rank, ?eltType)), b: remote(ndarray(rank, eltType))): remote(ndarray(rank, eltType))
+
+.. method:: proc type ndarray.convolve(features: ndarray(3, ?eltType), kernel: ndarray(4, eltType), stride: int): ndarray(3, eltType)
+
+.. method:: proc type ndarray.convolve(features: ndarray(3, ?eltType), kernel: ndarray(4, eltType), bias: ndarray(1, eltType), stride: int): ndarray(3, eltType)
+
+.. method:: proc type ndarray.maxPool(features: ndarray(3, ?eltType), poolSize: int): ndarray(3, eltType)
+
+.. method:: proc type ndarray.matvecmul(mat: ndarray(2, ?eltType), vec: ndarray(1, eltType)): ndarray(1, eltType)
+
+.. method:: proc type ndarray.fromRanges(type eltType = real, rngs: range ...?rank): ndarray(rank, eltType)
+
+.. method:: proc ndarray.serialize(writer: IO.fileWriter(locking = false, IO.defaultSerializer), ref serializer: IO.defaultSerializer)
+
+.. method:: proc ref ndarray.read(fr: IO.fileReader(?)) throws
+
+.. method:: proc ndarray.write(fw: IO.fileWriter(?)) throws
+
+.. class:: _tensor_resource
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real(64)
+
+   .. attribute:: var data: remote(ndarray(rank, eltType))
+
+.. method:: proc type ndarray.fullOuter(a: ndarray(?rankA, ?eltType), b: ndarray(?rankB, eltType)): ndarray(rankA+rankB, eltType)
+
+.. method:: proc type ndarray.contract(a: ndarray(?rankA, ?eltType), b: ndarray(?rankB, eltType), param axisA: int, param axisB: int)
+
+.. function:: proc splitAt(param s: string, param del: string, param idx: int = 0) param
+
+.. function:: proc getFirstIdx(param s: string, param del: string, param idx: int = 0) param
+
+.. function:: proc sliceGeneric(type t, param start: int, param stop: int, param s: t, param idx: int = start) param
+
+.. function:: proc slice(param start: int, param stop: int, param s: string, param idx: int = start) param
+
+.. function:: proc take(param count: int, param s: string) param
+
+.. function:: proc drop(param count: int, param s: string) param
+
+.. method:: proc type ndarray.einsum(param subscripts: string, a: ndarray(?rankA, ?eltType), b: ndarray(?rankB, eltType))
+
+.. function:: proc main()
+
diff --git a/docs/_sources/modules/lib/Network.rst.txt b/docs/_sources/modules/lib/Network.rst.txt
new file mode 100644
index 000000000..e7fc0350f
--- /dev/null
+++ b/docs/_sources/modules/lib/Network.rst.txt
@@ -0,0 +1,269 @@
+.. default-domain:: chpl
+
+.. module:: Network
+
+Network
+=======
+**Usage**
+
+.. code-block:: chapel
+
+   use Network;
+
+
+or
+
+.. code-block:: chapel
+
+   import Network;
+
+.. function:: proc helpFindModuleByName(arg, x: string): borrowed Module(?)?
+
+.. function:: proc helpFindParamDataByName(arg, x: string) ref: Tensor(?)
+
+.. record:: moduleChildren
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var childDict: map(string, borrowed Module(eltType))
+
+   .. attribute:: var order: list(string)
+
+   .. method:: proc init(type eltType = real)
+
+   .. itermethod:: iter ref these(): borrowed Module(eltType)
+
+   .. itermethod:: iter ref items(): (string, borrowed Module(eltType))
+
+   .. method:: proc ref add(name: string, m: borrowed Module(eltType))
+
+   .. method:: proc ref ith(i: int): borrowed Module(eltType)
+
+.. method:: proc class.this(fieldName: string): borrowed Module(?) where isSubtype(this.type, Module(?))
+
+.. method:: proc class.this(fieldName: string) ref: Tensor(?) where isSubtype(this.type, Parameter(?))
+
+.. itermethod:: iter class.moduleFieldNames(): string where isSubtype(this.type, Module(?))
+
+.. itermethod:: iter class.moduleFields(): (string, borrowed Module(?)) where isSubtype(this.type, Module(?))
+
+.. method:: proc class.registerModules() where isSubtype(this.type, Module(?))
+
+.. method:: proc class.postinit() where isSubtype(this.type, Module(?))
+
+.. record:: moduleAttributes : serializable
+
+   .. attribute:: var layerType: string
+
+   .. attribute:: var moduleName: string
+
+   .. attribute:: var attributes: dict(string, string)
+
+   .. method:: proc init(layerType: string, moduleName: string, in attrs: map(string, string, ?))
+
+   .. method:: proc init(layerType: string, moduleName: string, in attrs: dict(string, ?))
+
+   .. method:: proc init(layerType: string, moduleName: string, in attrs: map(string, ?valType, ?)) where valType != string
+
+   .. method:: proc init(layerType: string, moduleName: string, in attrs: map(string, ?valType, ?), order: list(string))
+
+   .. method:: proc init(layerType: string, moduleName: string, attrs ...?n) where attrs(0)(0).type == string
+
+   .. method:: proc init(layerType: string, moduleName: string)
+
+   .. method:: proc getInt(name: string): int
+
+   .. method:: proc prettyPrint(): string
+
+   .. method:: proc prettyPrintSpec(): string
+
+   .. method:: operator :(ma: moduleAttributes, type T: string)
+
+.. class:: ModuleSpecification : serializable
+
+   .. attribute:: var layerType: string
+
+   .. attribute:: var attributes: map(string, string)
+
+   .. attribute:: var subModules: map(string, owned ModuleSpecification?)
+
+   .. attribute:: var subModuleOrder: list(string)
+
+.. function:: proc moduleFromSpec(ms_: borrowed ModuleSpecification?): owned Module(real)
+
+.. function:: proc modelFromSpecFile(path: string): owned Module(real)
+
+.. data:: var moduleInstances = 0
+
+.. class:: Module
+
+   .. attribute:: type eltType = real
+
+   .. attribute:: var subModules: moduleChildren(eltType)
+
+   .. attribute:: var moduleId: int
+
+   .. attribute:: var moduleName: string
+
+   .. attribute:: var ownedModules: list(shared Module(eltType))
+
+   .. method:: proc init(type eltType = real)
+
+   .. method:: proc init(type eltType = real, ma: moduleAttributes)
+
+   .. method:: proc setup()
+
+   .. method:: proc this(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: proc getSubModuleName(name: string): string
+
+   .. method:: proc addModule(name: string, m: borrowed Module(eltType))
+
+   .. method:: proc addModule(name: string, m: shared Module(eltType))
+
+   .. method:: proc addModule(name: string, in m: owned Module(eltType))
+
+   .. method:: proc addParameter(name: string, data: Tensor(eltType))
+
+   .. method:: proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: proc par(paramName: string) ref: Tensor(eltType)
+
+   .. method:: proc mod(modName: string): borrowed Module(eltType)
+
+   .. itermethod:: iter parameters(): borrowed Parameter(eltType)
+
+   .. itermethod:: iter moduleNames(): string
+
+   .. itermethod:: iter parameterNames(): string
+
+   .. itermethod:: iter modules(): borrowed Module(eltType)
+
+   .. itermethod:: iter namedModules(): (string, borrowed Module(eltType))
+
+   .. method:: proc loadPyTorchDump(modelPath: string, param debug = false)
+
+   .. method:: proc attributes(): moduleAttributes
+
+   .. method:: proc signature: string
+
+.. class:: Parameter : Module(?)
+
+   .. attribute:: var data: Tensor(eltType)
+
+   .. method:: proc init(data: Tensor(?eltType))
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Sequential : Module(?)
+
+   .. attribute:: var mds: list(shared Module(eltType))
+
+   .. method:: proc init(type eltType = real, ms: dict(string, shared Module(eltType)), param overrideName = false, moduleName: string = "")
+
+   .. method:: proc init(type eltType = real, in ms)
+
+   .. method:: proc init(type eltType = real, order: list(string), in ms: map(string, owned Module(eltType)?))
+
+   .. method:: proc init(in ms: owned Module(real)? ...?rank)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Linear : Module(?)
+
+   .. attribute:: var m: int
+
+   .. attribute:: var n: m.type
+
+   .. attribute:: var weight: owned Parameter(eltType)
+
+   .. attribute:: var bias: owned Parameter(eltType)
+
+   .. method:: proc init(type eltType, m: int, n: int)
+
+   .. method:: override proc setup()
+
+   .. method:: proc init(m: int, n: int)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Conv2D : Module(?)
+
+   .. attribute:: var kernelShape: 4*(int)
+
+   .. attribute:: var stride: int
+
+   .. attribute:: var kernel: owned Parameter(eltType)
+
+   .. attribute:: var bias: owned Parameter(eltType)
+
+   .. method:: proc init(type eltType = real, channels: int, features: int, kernel: int, stride: int = 1)
+
+   .. method:: proc init(type eltType = real, ma: moduleAttributes)
+
+   .. method:: override proc setup()
+
+   .. method:: proc init(channels: int, features: int, kernel: int, stride: int = 1)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: MaxPool : Module(?)
+
+   .. attribute:: var poolSize: int
+
+   .. method:: proc init(type eltType = real, poolSize: int)
+
+   .. method:: proc init(poolSize: int)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Flatten : Module(?)
+
+   .. method:: proc init(type eltType = real)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: ReLU : Module(?)
+
+   .. method:: proc init(type eltType = real)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Softmax : Module(?)
+
+   .. method:: proc init(type eltType = real)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. class:: Dropout : Module(?)
+
+   .. method:: proc init(type eltType = real, freq: real = 0.5)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+   .. method:: override proc attributes(): moduleAttributes
+
+.. function:: proc chain(m: borrowed Module(?), modNames: string ...?n, input: Tensor(?eltType))
+
+.. class:: Net : Module(?)
+
+   .. method:: proc init(type eltType = real)
+
+   .. method:: override proc forward(input: Tensor(eltType)): Tensor(eltType)
+
+.. function:: proc main()
+
diff --git a/docs/_sources/modules/lib/OrderedDict.rst.txt b/docs/_sources/modules/lib/OrderedDict.rst.txt
new file mode 100644
index 000000000..57540ae8a
--- /dev/null
+++ b/docs/_sources/modules/lib/OrderedDict.rst.txt
@@ -0,0 +1,45 @@
+.. default-domain:: chpl
+
+.. module:: OrderedDict
+
+OrderedDict
+===========
+**Usage**
+
+.. code-block:: chapel
+
+   use OrderedDict;
+
+
+or
+
+.. code-block:: chapel
+
+   import OrderedDict;
+
+.. record:: dict
+
+   .. attribute:: type keyType
+
+   .. attribute:: type valType
+
+   .. attribute:: var table: map(keyType, valType)
+
+   .. attribute:: var order: list(keyType)
+
+   .. method:: proc init(in table: map(?keyType, ?valType), in order: list(keyType))
+
+   .. method:: proc init(in table: map(?keyType, ?valType))
+
+   .. method:: proc init(type keyType, type valType)
+
+   .. method:: proc size: int
+
+   .. itermethod:: iter keys(): keyType
+
+   .. itermethod:: iter values(): valType
+
+   .. itermethod:: iter ref these()
+
+   .. method:: proc ref insert(key: keyType, in value: valType)
+
diff --git a/docs/_sources/modules/lib/Remote.rst.txt b/docs/_sources/modules/lib/Remote.rst.txt
new file mode 100644
index 000000000..f1f086caa
--- /dev/null
+++ b/docs/_sources/modules/lib/Remote.rst.txt
@@ -0,0 +1,67 @@
+.. default-domain:: chpl
+
+.. module:: Remote
+
+Remote
+======
+**Usage**
+
+.. code-block:: chapel
+
+   use Remote;
+
+
+or
+
+.. code-block:: chapel
+
+   import Remote;
+
+.. data:: config const debug = true
+
+.. data:: var defaultDevice = if here.gpus.size >= 1 then here.gpus[0] else here
+
+.. class:: Remote : serializable
+
+   .. attribute:: type eltType
+
+   .. attribute:: var device: locale = defaultDevice
+
+   .. attribute:: var item: _remoteVarWrapper(eltType)
+
+   .. method:: proc init(item: ?eltType, device: locale = defaultDevice)
+
+   .. method:: proc init(type eltType, device: locale = defaultDevice)
+
+   .. method:: proc to(device_: locale)
+
+   .. method:: proc ref get() ref
+
+   .. method:: proc serialize(writer, serializer)
+
+.. record:: remote : serializable
+
+   .. attribute:: type eltType
+
+   .. attribute:: var remoteResource: shared Remote(eltType)
+
+   .. method:: proc init(type eltType, device: locale = defaultDevice)
+
+   .. method:: proc init(item: ?eltType, device: locale = defaultDevice)
+
+   .. method:: proc device ref
+
+   .. method:: proc ref get() ref
+
+   .. method:: proc to(device_: locale)
+
+   .. method:: proc copy(): remote(eltType)
+
+   .. method:: proc ref access() ref
+
+   .. method:: proc localAccess() ref
+
+   .. method:: proc download(): eltType
+
+   .. method:: proc ref unload(): eltType
+
diff --git a/docs/_sources/modules/lib/SimpleDomain.rst.txt b/docs/_sources/modules/lib/SimpleDomain.rst.txt
new file mode 100644
index 000000000..d09231bc9
--- /dev/null
+++ b/docs/_sources/modules/lib/SimpleDomain.rst.txt
@@ -0,0 +1,33 @@
+.. default-domain:: chpl
+
+.. module:: SimpleDomain
+
+SimpleDomain
+============
+**Usage**
+
+.. code-block:: chapel
+
+   use SimpleDomain;
+
+
+or
+
+.. code-block:: chapel
+
+   import SimpleDomain;
+
+.. function:: proc zeroTup(param rank: int)
+
+.. record:: rect
+
+   .. attribute:: param rank: int
+
+   .. attribute:: var shape: rank*int
+
+   .. attribute:: var offset: rank*int = zeroTup(rank)
+
+   .. method:: proc init(shape: int ...?rank)
+
+   .. method:: proc size: int
+
diff --git a/docs/_sources/modules/lib/StaticTensor.rst.txt b/docs/_sources/modules/lib/StaticTensor.rst.txt
new file mode 100644
index 000000000..bc137289a
--- /dev/null
+++ b/docs/_sources/modules/lib/StaticTensor.rst.txt
@@ -0,0 +1,133 @@
+.. default-domain:: chpl
+
+.. module:: StaticTensor
+
+StaticTensor
+============
+**Usage**
+
+.. code-block:: chapel
+
+   use StaticTensor;
+
+
+or
+
+.. code-block:: chapel
+
+   import StaticTensor;
+
+.. record:: tensor : serializable
+
+   .. attribute:: param rank: int
+
+   .. attribute:: type eltType = real(64)
+
+   .. attribute:: var resource: shared BaseTensorResource(eltType, rank)
+
+   .. method:: proc meta
+
+   .. method:: proc _dom
+
+   .. method:: proc init(param rank: int, type eltType = real(64))
+
+   .. method:: proc init(resource: shared BaseTensorResource(?eltType, ?rank), param strict: bool = false)
+
+   .. method:: proc init(nda: ndarray(?rank, ?eltType))
+
+   .. method:: proc init(dom: domain(?), type eltType = real)
+
+   .. method:: proc init(arr: [] ?eltType)
+
+   .. method:: proc init(it: _iteratorRecord)
+
+   .. method:: proc this(args ...)
+
+   .. method:: proc reshapeDomain(dom: this._dom.type)
+
+   .. method:: proc _setArrayData(value)
+
+.. function:: proc tensorFromCtx(param rank: int, type eltType, ctx): tensor(rank, eltType)
+
+.. function:: operator +(a: tensor(?rank, ?eltType), b: tensor(rank, eltType))
+
+.. function:: operator -(a: tensor(?rank, ?eltType), b: tensor(rank, eltType))
+
+.. function:: operator *(a: tensor(?rank, ?eltType), b: tensor(rank, eltType))
+
+.. function:: operator /(a: tensor(?rank, ?eltType), b: tensor(rank, eltType))
+
+.. method:: proc tensor.reshape(dom: domain(?))
+
+.. method:: proc tensor.reshape(newShape: int ...?newRank)
+
+.. method:: proc tensor.relu()
+
+.. method:: proc tensor.permute(axes: int ...rank)
+
+.. method:: proc tensor.expand(axes: int ...rank)
+
+.. method:: proc tensor.pad(args: 2*int ...rank, value: eltType = 0.0)
+
+.. method:: proc tensor.shrink(args: 2*int ...rank)
+
+.. method:: proc tensor.slice(dom: domain(?)) where dom.rank == rank
+
+.. method:: proc tensor.slice(rngs: range ...rank)
+
+.. method:: proc tensor.sum(axes: int ...?r)
+
+.. method:: proc tensor.unsqueeze(dim: int): tensor(rank+1, eltType)
+
+.. method:: proc tensor.max(): tensor(1, eltType)
+
+.. method:: proc tensor.exp(): tensor(rank, eltType)
+
+.. method:: proc tensor.softmax(): tensor(rank, eltType)
+
+.. function:: proc matvec(mat: tensor(2, ?eltType), vec: tensor(1, eltType)): tensor(1, eltType)
+
+.. function:: proc matvec(mat: tensor(2, ?eltType), vec: tensor(2, eltType)): tensor(2, eltType)
+
+.. method:: proc type tensor.matvecmul(m, v)
+
+.. method:: proc type tensor.convolve(features: tensor(3, ?eltType), kernel: tensor(4, eltType), stride: int): tensor(3, eltType)
+
+.. method:: proc type tensor.convolve(features: tensor(3, ?eltType), kernel: tensor(4, eltType), bias: tensor(1, eltType), stride: int): tensor(3, eltType)
+
+.. method:: proc type tensor.matvecmulFast(mat: tensor(2, ?eltType), vec: tensor(1, eltType)): tensor(1, eltType)
+
+.. method:: proc tensor.dilate(dil: int): tensor(3, eltType) where this.rank == 3
+
+.. method:: proc tensor.maxPool(poolSize: int): tensor(3, eltType) where this.rank == 3
+
+.. method:: proc type tensor.arange(to: int, type eltType = real, shape: ?rank*int): tensor(rank, eltType)
+
+.. method:: proc type tensor.arange(shape: int ...?rank): tensor(rank, real)
+
+.. method:: proc type tensor.fromShape(type eltType = real, shape: int ...?rank, value: eltType = 0: eltType): tensor(rank, eltType)
+
+.. method:: proc type tensor.zeros(shape: int ...?rank): tensor(rank, real)
+
+.. method:: proc type tensor.zeros(type eltType, shape: int ...?rank): tensor(rank, eltType)
+
+.. method:: proc type tensor.ones(shape: int ...?rank): tensor(rank, real)
+
+.. method:: proc type tensor.ones(type eltType, shape: int ...?rank): tensor(rank, eltType)
+
+.. data:: config const n = 100
+
+.. data:: config const diag = false
+
+.. data:: config const size = 3
+
+.. function:: proc main()
+
+.. method:: proc tensor.serialize(writer: IO.fileWriter(locking = false, IO.defaultSerializer), ref serializer: IO.defaultSerializer)
+
+.. method:: proc tensor.serialize(writer: IO.fileWriter(?), ref serializer: ?srt2) where srt2 != IO.defaultSerializer
+
+.. method:: proc tensor.serialize(writer: IO.fileWriter(locking = false, IO.defaultSerializer), ref serializer: IO.defaultSerializer, param capitalT: bool) where capitalT == true
+
+.. method:: proc ref tensor.read(fr: IO.fileReader(?)) throws
+
diff --git a/docs/_sources/modules/lib/Tensor.rst.txt b/docs/_sources/modules/lib/Tensor.rst.txt
new file mode 100644
index 000000000..639901134
--- /dev/null
+++ b/docs/_sources/modules/lib/Tensor.rst.txt
@@ -0,0 +1,19 @@
+.. default-domain:: chpl
+
+.. module:: Tensor
+
+Tensor
+======
+**Usage**
+
+.. code-block:: chapel
+
+   use Tensor;
+
+
+or
+
+.. code-block:: chapel
+
+   import Tensor;
+
diff --git a/docs/_sources/modules/lib/Utilities.rst.txt b/docs/_sources/modules/lib/Utilities.rst.txt
new file mode 100644
index 000000000..349931298
--- /dev/null
+++ b/docs/_sources/modules/lib/Utilities.rst.txt
@@ -0,0 +1,79 @@
+.. default-domain:: chpl
+
+.. module:: Utilities
+
+Utilities
+=========
+**Usage**
+
+.. code-block:: chapel
+
+   use Utilities;
+
+
+or
+
+.. code-block:: chapel
+
+   import Utilities;
+
+**Submodules**
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   Utilities/*
+
+.. data:: config param loopGpuSupport = false
+
+.. data:: param debugPrint = false
+
+.. function:: proc err(args ...?n)
+
+.. function:: proc debugWrite(args ...?n)
+
+.. iterfunction:: iter cartesian(X, Y)
+
+.. iterfunction:: iter cartesian(param tag: iterKind, X, Y) where tag == iterKind.standalone
+
+.. iterfunction:: iter flatIter(D: domain(?))
+
+.. iterfunction:: iter flatIter(param tag: iterKind, D: domain(?)) where tag == iterKind.standalone
+
+.. function:: proc normalizeArray(arr: [])
+
+.. function:: proc normalizeArray(unknown: ?t) where !isArrayType(t)
+
+.. function:: proc normalizeDomain(dom: domain(?)): domain(dom.rank, int)
+
+.. function:: proc emptyDomain(param rank: int): domain(rank, int)
+
+.. function:: proc domainFromShape(shape: int ...?rank): domain(rank, int)
+
+.. function:: proc rangeFromBound(high: int): range
+
+.. function:: proc rangeFromBound(low: int, high: int): range
+
+.. function:: proc rangeFromStart(start: int, count: int)
+
+.. function:: proc rangesFromBounds(bounds: 2*int ...?rank): rank*range
+
+.. function:: proc rangesFromShape(shape: int ...?rank): rank*range
+
+.. function:: proc nbase(bounds: ?rank*int, n: int): rank*int
+
+.. function:: proc indexInShape(shape: ?rank*int, in n: int): rank*int
+
+.. function:: proc indexAtHelperProd(n: int, prod: int, shape: int ...?rank): rank*int where rank > 1
+
+.. function:: proc indexAtHelperMultiples(n: int, divs: int ...?rank): rank*int
+
+.. function:: proc indexAt(n: int, shape: int)
+
+.. function:: proc indexAt(n: int, shape: int ...?rank): rank*int where rank > 1
+
+.. iterfunction:: iter fastEach(shape: int ...?rank): rank*int
+
+.. function:: proc argsort(tup: int ...?rank)
+
diff --git a/docs/_sources/modules/lib/Utilities/Standard.rst.txt b/docs/_sources/modules/lib/Utilities/Standard.rst.txt
new file mode 100644
index 000000000..456de407a
--- /dev/null
+++ b/docs/_sources/modules/lib/Utilities/Standard.rst.txt
@@ -0,0 +1,77 @@
+.. default-domain:: chpl
+
+.. module:: Standard
+
+Standard
+========
+**Usage**
+
+.. code-block:: chapel
+
+   use Utilities.Standard;
+
+
+or
+
+.. code-block:: chapel
+
+   import Utilities.Standard;
+
+.. method:: proc _tuple.imageType(f) type
+
+.. method:: proc _tuple.eltType type where isHomogeneousTuple(this)
+
+.. method:: proc _tuple.map(f): this.size*this.imageType(f) where isHomogeneousTuple(this)
+
+.. method:: proc _tuple.slice(param low: int, param high: int): (high-low)*this.eltType where isHomogeneousTuple(this) && low < high && 0 <= low && high <= this.size
+
+.. method:: proc _tuple.removeIdx(param idx: int): (this.size-1)*this.eltType
+
+.. method:: proc _tuple.untuplify()
+
+.. method:: proc _tuple.slice(param start: int, param stop: int, param idx: int = start) param
+
+.. method:: proc _tuple.insertIdx(param idx: int, x: this.eltType): (this.size+1)*this.eltType
+
+.. method:: proc _tuple.indexAt(n: int): this.size*int where isHomogeneousTuple(this) && this(0).type == int
+
+.. itermethod:: iter _tuple.each(n: int): this.size*int where isHomogeneousTuple(this) && this(0).type == int
+
+.. itermethod:: iter _domain.each
+
+.. itermethod:: iter _domain.every() where rank == 1
+
+.. itermethod:: iter _domain.every(param tag: iterKind) where tag == iterKind.standalone && rank == 1
+
+.. itermethod:: iter _domain.every() where rank > 1
+
+.. itermethod:: iter _domain.every(param tag: iterKind) where tag == iterKind.standalone && rank > 1
+
+.. itermethod:: iter _domain.everyZip()
+
+.. itermethod:: iter _domain.everyZip(param tag: iterKind) where tag == iterKind.standalone
+
+.. method:: proc _domain.indexAt(n: int) where rank == 1
+
+.. method:: proc _domain.indexAt(n: int) where rank > 1
+
+.. method:: proc _domain.fastShape
+
+.. method:: proc _domain.myShape
+
+.. method:: proc _domain.normalize: this.type where this.isRectangular()
+
+.. method:: proc _domain.isNormal: bool where this.isRectangular()
+
+.. method:: proc param string.this(param start: int, param stop: int) param
+
+.. method:: proc param string.slice(param start: int, param stop: int, param idx: int = start) param
+
+.. method:: proc param string.take(param count: int) param
+
+.. method:: proc param string.drop(param count: int) param
+
+.. method:: proc param string.countOccurrences(param c: string, param idx: int = 0) param
+
+.. method:: proc param string.takeUntil(param del: string, param idx: int = 0, param keepDel: bool = false) param
+
diff --git a/docs/_sources/modules/lib/Utilities/Types.rst.txt b/docs/_sources/modules/lib/Utilities/Types.rst.txt
new file mode 100644
index 000000000..8d9d26264
--- /dev/null
+++ b/docs/_sources/modules/lib/Utilities/Types.rst.txt
@@ -0,0 +1,21 @@
+.. default-domain:: chpl
+
+.. module:: Types
+
+Types
+=====
+**Usage**
+
+.. code-block:: chapel
+
+   use Utilities.Types;
+
+
+or
+
+.. code-block:: chapel
+
+   import Utilities.Types;
+
+.. type:: type stdRange = range(idxType = int, bounds = boundKind.both, strides = strideKind.one)
+
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
new file mode 100644
index 000000000..30fee9d0f
--- /dev/null
+++ b/docs/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
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+
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+    clear: left;
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+
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+
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+    font-size: 90%;
+}
+
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+    display: none;
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+
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+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
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+    max-width: 100%;
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+    margin-bottom: 10px;
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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
new file mode 100644
index 000000000..d06a71d75
--- /dev/null
+++ b/docs/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
new file mode 100644
index 000000000..d1f229197
--- /dev/null
+++ b/docs/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.0.1',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/docs/_static/file.png b/docs/_static/file.png
new file mode 100644
index 000000000..a858a410e
Binary files /dev/null and b/docs/_static/file.png differ
diff --git a/docs/_static/js/badge_only.js b/docs/_static/js/badge_only.js
new file mode 100644
index 000000000..526d7234b
--- /dev/null
+++ b/docs/_static/js/badge_only.js
@@ -0,0 +1 @@
+!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});
\ No newline at end of file
diff --git a/docs/_static/js/html5shiv-printshiv.min.js b/docs/_static/js/html5shiv-printshiv.min.js
new file mode 100644
index 000000000..2b43bd062
--- /dev/null
+++ b/docs/_static/js/html5shiv-printshiv.min.js
@@ -0,0 +1,4 @@
+/**
+* @preserve HTML5 Shiv 3.7.3-pre | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=y.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=y.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),y.elements=c+" "+a,j(b)}function f(a){var b=x[a[v]];return b||(b={},w++,a[v]=w,x[w]=b),b}function g(a,c,d){if(c||(c=b),q)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():u.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||t.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),q)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return y.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(y,b.frag)}function j(a){a||(a=b);var d=f(a);return!y.shivCSS||p||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),q||i(a,d),a}function k(a){for(var b,c=a.getElementsByTagName("*"),e=c.length,f=RegExp("^(?:"+d().join("|")+")$","i"),g=[];e--;)b=c[e],f.test(b.nodeName)&&g.push(b.applyElement(l(b)));return g}function l(a){for(var b,c=a.attributes,d=c.length,e=a.ownerDocument.createElement(A+":"+a.nodeName);d--;)b=c[d],b.specified&&e.setAttribute(b.nodeName,b.nodeValue);return e.style.cssText=a.style.cssText,e}function m(a){for(var b,c=a.split("{"),e=c.length,f=RegExp("(^|[\\s,>+~])("+d().join("|")+")(?=[[\\s,>+~#.:]|$)","gi"),g="$1"+A+"\\:$2";e--;)b=c[e]=c[e].split("}"),b[b.length-1]=b[b.length-1].replace(f,g),c[e]=b.join("}");return c.join("{")}function n(a){for(var b=a.length;b--;)a[b].removeNode()}function o(a){function b(){clearTimeout(g._removeSheetTimer),d&&d.removeNode(!0),d=null}var d,e,g=f(a),h=a.namespaces,i=a.parentWindow;return!B||a.printShived?a:("undefined"==typeof h[A]&&h.add(A),i.attachEvent("onbeforeprint",function(){b();for(var f,g,h,i=a.styleSheets,j=[],l=i.length,n=Array(l);l--;)n[l]=i[l];for(;h=n.pop();)if(!h.disabled&&z.test(h.media)){try{f=h.imports,g=f.length}catch(o){g=0}for(l=0;g>l;l++)n.push(f[l]);try{j.push(h.cssText)}catch(o){}}j=m(j.reverse().join("")),e=k(a),d=c(a,j)}),i.attachEvent("onafterprint",function(){n(e),clearTimeout(g._removeSheetTimer),g._removeSheetTimer=setTimeout(b,500)}),a.printShived=!0,a)}var p,q,r="3.7.3",s=a.html5||{},t=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,u=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,v="_html5shiv",w=0,x={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",p="hidden"in a,q=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){p=!0,q=!0}}();var y={elements:s.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:r,shivCSS:s.shivCSS!==!1,supportsUnknownElements:q,shivMethods:s.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=y,j(b);var z=/^$|\b(?:all|print)\b/,A="html5shiv",B=!q&&function(){var c=b.documentElement;return!("undefined"==typeof b.namespaces||"undefined"==typeof b.parentWindow||"undefined"==typeof c.applyElement||"undefined"==typeof c.removeNode||"undefined"==typeof a.attachEvent)}();y.type+=" print",y.shivPrint=o,o(b),"object"==typeof module&&module.exports&&(module.exports=y)}("undefined"!=typeof window?window:this,document);
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diff --git a/docs/_static/js/html5shiv.min.js b/docs/_static/js/html5shiv.min.js
new file mode 100644
index 000000000..cd1c674f5
--- /dev/null
+++ b/docs/_static/js/html5shiv.min.js
@@ -0,0 +1,4 @@
+/**
+* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
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diff --git a/docs/_static/js/theme.js b/docs/_static/js/theme.js
new file mode 100644
index 000000000..1fddb6ee4
--- /dev/null
+++ b/docs/_static/js/theme.js
@@ -0,0 +1 @@
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<button class="toctree-expand" title="Open/close menu"></button>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
new file mode 100644
index 000000000..250f5665f
--- /dev/null
+++ b/docs/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/docs/_static/minus.png b/docs/_static/minus.png
new file mode 100644
index 000000000..d96755fda
Binary files /dev/null and b/docs/_static/minus.png differ
diff --git a/docs/_static/plus.png b/docs/_static/plus.png
new file mode 100644
index 000000000..7107cec93
Binary files /dev/null and b/docs/_static/plus.png differ
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
new file mode 100644
index 000000000..0d49244ed
--- /dev/null
+++ b/docs/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
new file mode 100644
index 000000000..7918c3fab
--- /dev/null
+++ b/docs/_static/searchtools.js
@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js
new file mode 100644
index 000000000..8a96c69a1
--- /dev/null
+++ b/docs/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/docs/chpl-modindex.html b/docs/chpl-modindex.html
new file mode 100644
index 000000000..dfded9bca
--- /dev/null
+++ b/docs/chpl-modindex.html
@@ -0,0 +1,232 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Chapel Module Index &mdash; chpldoc 0.0.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/documentation_options.js?v=d45e8c67"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+ 
+
+    <script>
+      DOCUMENTATION_OPTIONS.COLLAPSE_INDEX = true;
+    </script>
+
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            chpldoc
+          </a>
+              <div class="version">
+                0.0.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="index.html">chpldoc documentation</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Autograd.html">Autograd</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/DynamicTensor.html">DynamicTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/LoadNumpy.html">LoadNumpy</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/NDArray.html">NDArray</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Network.html">Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/OrderedDict.html">OrderedDict</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Remote.html">Remote</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/SimpleDomain.html">SimpleDomain</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/StaticTensor.html">StaticTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Tensor.html">Tensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Standard.html">Standard</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Types.html">Types</a></li>
+</ul>
+
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+
+   <h1>Chapel Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-a"><strong>a</strong></a> | 
+   <a href="#cap-d"><strong>d</strong></a> | 
+   <a href="#cap-l"><strong>l</strong></a> | 
+   <a href="#cap-n"><strong>n</strong></a> | 
+   <a href="#cap-o"><strong>o</strong></a> | 
+   <a href="#cap-r"><strong>r</strong></a> | 
+   <a href="#cap-s"><strong>s</strong></a> | 
+   <a href="#cap-t"><strong>t</strong></a> | 
+   <a href="#cap-u"><strong>u</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-a"><td></td><td>
+       <strong>a</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Autograd.html#module-Autograd"><code class="xref">Autograd</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-d"><td></td><td>
+       <strong>d</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/DynamicTensor.html#module-DynamicTensor"><code class="xref">DynamicTensor</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-l"><td></td><td>
+       <strong>l</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/LoadNumpy.html#module-LoadNumpy"><code class="xref">LoadNumpy</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-n"><td></td><td>
+       <strong>n</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/NDArray.html#module-NDArray"><code class="xref">NDArray</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Network.html#module-Network"><code class="xref">Network</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-o"><td></td><td>
+       <strong>o</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/OrderedDict.html#module-OrderedDict"><code class="xref">OrderedDict</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-r"><td></td><td>
+       <strong>r</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Remote.html#module-Remote"><code class="xref">Remote</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-s"><td></td><td>
+       <strong>s</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/SimpleDomain.html#module-SimpleDomain"><code class="xref">SimpleDomain</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Utilities/Standard.html#module-Standard"><code class="xref">Standard</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/StaticTensor.html#module-StaticTensor"><code class="xref">StaticTensor</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-t"><td></td><td>
+       <strong>t</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Tensor.html#module-Tensor"><code class="xref">Tensor</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Utilities/Types.html#module-Types"><code class="xref">Types</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-u"><td></td><td>
+       <strong>u</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="modules/lib/Utilities.html#module-Utilities"><code class="xref">Utilities</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
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+<html class="writer-html5" lang="en" data-content_root="./">
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+<li class="toctree-l1"><a class="reference internal" href="index.html">chpldoc documentation</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Autograd.html">Autograd</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/DynamicTensor.html">DynamicTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/LoadNumpy.html">LoadNumpy</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/NDArray.html">NDArray</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Network.html">Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/OrderedDict.html">OrderedDict</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Remote.html">Remote</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/SimpleDomain.html">SimpleDomain</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/StaticTensor.html">StaticTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Tensor.html">Tensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Standard.html">Standard</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Types.html">Types</a></li>
+</ul>
+
+        </div>
+      </div>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#Symbols"><strong>Symbols</strong></a>
+ | <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
+ 
+</div>
+<h2 id="Symbols">Symbols</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.*">*() (in module DynamicTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.*">(in module NDArray)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.*">(in module StaticTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.+">+() (in module DynamicTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.+">(in module NDArray)</a>, <a href="modules/lib/NDArray.html#NDArray.+">[1]</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.+">(in module StaticTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.-">-() (in module DynamicTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.-">(in module NDArray)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.-">(in module StaticTensor)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray./">/() (in module NDArray)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor./">(in module StaticTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.:">:() (in module DynamicTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.:">(in module NDArray)</a>, <a href="modules/lib/NDArray.html#NDArray.:">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.moduleAttributes.:">(Network.moduleAttributes method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.=">=() (in module NDArray)</a>, <a href="modules/lib/NDArray.html#NDArray.=">[1]</a>, <a href="modules/lib/NDArray.html#NDArray.=">[2]</a>, <a href="modules/lib/NDArray.html#NDArray.=">[3]</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor._dom">_dom() (StaticTensor.tensor method)</a>
+</li>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.ArrClass._domain">_domain (LoadNumpy.ArrClass attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData._domain">(NDArray.NDArrayData attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource._loc">_loc() (Autograd.BaseTensorResource method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reluOp._relu">_relu() (Autograd.reluOp method)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor._setArrayData">_setArrayData() (StaticTensor.tensor method)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray._tensor_resource">_tensor_resource (class in NDArray)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Remote.html#Remote.remote.access">access() (Remote.remote method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.add">add() (Network.moduleChildren method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.addModule">addModule() (Network.Module method)</a>, <a href="modules/lib/Network.html#Network.Module.addModule">[1]</a>, <a href="modules/lib/Network.html#Network.Module.addModule">[2]</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp">addOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.addParameter">addParameter() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.arange">arange() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.arange">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.arange">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.arange">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.padOp.arg">arg (Autograd.padOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.arg">(Autograd.shrinkOp attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.argmax">argmax() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.argmax">(NDArray.ndarray method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Utilities.html#Utilities.argsort">argsort() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.array">array() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.array">(DynamicTensor.Tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.arrayResource">arrayResource (NDArray.ndarray attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.ArrClass">ArrClass (class in LoadNumpy)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes.attributes">attributes (Network.moduleAttributes attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.ModuleSpecification.attributes">(Network.ModuleSpecification attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.Conv2D.attributes">attributes() (Network.Conv2D method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Dropout.attributes">(Network.Dropout method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Flatten.attributes">(Network.Flatten method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Linear.attributes">(Network.Linear method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.MaxPool.attributes">(Network.MaxPool method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.attributes">(Network.Module method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Parameter.attributes">(Network.Parameter method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.ReLU.attributes">(Network.ReLU method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Sequential.attributes">(Network.Sequential method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Softmax.attributes">(Network.Softmax method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#module-Autograd">Autograd (module)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.maxOp.axes">axes (Autograd.maxOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.axes">(Autograd.sumOp attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.backward">backward() (Autograd.addOp method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.backward">(Autograd.BaseTensorResource method)</a>, <a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.backward">[1]</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.backward">(Autograd.conv2DOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expandOp.backward">(Autograd.expandOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.backward">(Autograd.layerSliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.backward">(Autograd.maxOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.backward">(Autograd.multOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.backward">(Autograd.padOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.backward">(Autograd.permuteOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.backward">(Autograd.reshapeOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.backward">(Autograd.shrinkOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.backward">(Autograd.sliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.backward">(Autograd.sumOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.TensorResource.backward">(Autograd.TensorResource method)</a>, <a href="modules/lib/Autograd.html#Autograd.TensorResource.backward">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.base">base (Autograd.layerSliceOp attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource">BaseTensorResource (class in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.baseValue">baseValue (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.batchify">batchify() (NDArray.ndarray iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Conv2D.bias">bias (Network.Conv2D attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Linear.bias">(Network.Linear attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.borrowResource">borrowResource() (NDArray.ndarray method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities.html#Utilities.cartesian">cartesian() (in module Utilities)</a>, <a href="modules/lib/Utilities.html#Utilities.cartesian">[1]</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.chain">chain() (in module Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.checkRank">checkRank() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.checkRank">(in module Autograd)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.childDict">childDict (Network.moduleChildren attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.children">children() (Autograd.addOp method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.baseValue.children">(Autograd.baseValue method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.children">(Autograd.conv2DOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expandOp.children">(Autograd.expandOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expOp.children">(Autograd.expOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.children">(Autograd.layerSliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.children">(Autograd.maxOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.children">(Autograd.multOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.children">(Autograd.padOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.children">(Autograd.permuteOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reluOp.children">(Autograd.reluOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.children">(Autograd.reshapeOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.children">(Autograd.shrinkOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.children">(Autograd.sliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.children">(Autograd.sumOp method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.contract">contract() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Conv2D">Conv2D (class in Network)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp">conv2DOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.convolve">convolve() (DynamicTensor.Tensor method)</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.convolve">[1]</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.convolve">(NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.convolve">[1]</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.convolve">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.convolve">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Remote.html#Remote.remote.copy">copy() (Remote.remote method)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard.string.countOccurrences">countOccurrences() (Standard.string method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.ArrClass.data">data (LoadNumpy.ArrClass attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray._tensor_resource.data">(NDArray._tensor_resource attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData.data">(NDArray.NDArrayData attribute)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Parameter.data">(Network.Parameter attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.data">data() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.data">(DynamicTensor.Tensor method)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.data">(NDArray.ndarray method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.dataResource">dataResource (Autograd.BaseTensorResource attribute)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.debug">debug (in module Remote)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.debugPrint">debugPrint (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.debugWrite">debugWrite() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.defaultDevice">defaultDevice (in module Remote)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.Remote.device">device (Remote.Remote attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.device">device() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.device">(DynamicTensor.Tensor method)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.remote.device">(Remote.remote method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.diag">diag (in module StaticTensor)</a>
+</li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict">dict (record in OrderedDict)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.dilate">dilate() (NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.dilate">[1]</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.dilate">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.divOp">divOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.dom">dom (Autograd.sliceOp attribute)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.domainFromShape">domainFromShape() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.remote.download">download() (Remote.remote method)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.drop">drop() (in module NDArray)</a>
+
+      <ul>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard.string.drop">(Standard.string method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.Dropout">Dropout (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#module-DynamicTensor">DynamicTensor (module)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.each">each() (Standard._domain iterator)</a>
+
+      <ul>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.each">(Standard._tuple iterator)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.einsum">einsum() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.eltType">eltType (Autograd.addOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.eltType">(Autograd.conv2DOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expandOp.eltType">(Autograd.expandOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.eltType">(Autograd.layerSliceOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.eltType">(Autograd.maxOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.eltType">(Autograd.multOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.eltType">(Autograd.padOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.eltType">(Autograd.permuteOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.eltType">(Autograd.reshapeOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.eltType">(Autograd.shrinkOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.eltType">(Autograd.sliceOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.eltType">(Autograd.sumOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.TensorEssence.eltType">(Autograd.TensorEssence attribute)</a>
+</li>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.eltType">(DynamicTensor.Tensor attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray._tensor_resource.eltType">(NDArray._tensor_resource attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.eltType">(NDArray.ndarray attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData.eltType">(NDArray.NDArrayData attribute)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.eltType">(Network.Module attribute)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.moduleChildren.eltType">(Network.moduleChildren attribute)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.Remote.eltType">(Remote.Remote attribute)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.remote.eltType">(Remote.remote attribute)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.eltType">(StaticTensor.tensor attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.eltType">eltType() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.emptyDomain">emptyDomain() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.tensor.eraseRank">eraseRank() (DynamicTensor.tensor method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.err">err() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.every">every() (Standard._domain iterator)</a>, <a href="modules/lib/Utilities/Standard.html#Standard._domain.every">[1]</a>, <a href="modules/lib/Utilities/Standard.html#Standard._domain.every">[2]</a>, <a href="modules/lib/Utilities/Standard.html#Standard._domain.every">[3]</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.everyZip">everyZip() (Standard._domain iterator)</a>, <a href="modules/lib/Utilities/Standard.html#Standard._domain.everyZip">[1]</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.exp">exp() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.exp">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.expand">expand() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.expand">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.expandOp.expandedShape">expandedShape (Autograd.expandOp attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.expandOp">expandOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.expOp">expOp (record in Autograd)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities.html#Utilities.fastEach">fastEach() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.fastShape">fastShape() (Standard._domain method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.features">features (Autograd.conv2DOp attribute)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.flatIter">flatIter() (in module Utilities)</a>, <a href="modules/lib/Utilities.html#Utilities.flatIter">[1]</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Flatten">Flatten (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.flatten">flatten() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.forceRank">forceRank() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.forceRank">(in module Autograd)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.forceRankMeta">forceRankMeta() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.ForgetfulTensor">ForgetfulTensor (class in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.forward">forward() (Autograd.addOp method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.forward">(Autograd.BaseTensorResource method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.baseValue.forward">(Autograd.baseValue method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.forward">(Autograd.conv2DOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.divOp.forward">(Autograd.divOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expandOp.forward">(Autograd.expandOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expOp.forward">(Autograd.expOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.forward">(Autograd.layerSliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.forward">(Autograd.maxOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.forward">(Autograd.multOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.forward">(Autograd.padOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.forward">(Autograd.permuteOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reluOp.forward">(Autograd.reluOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.forward">(Autograd.reshapeOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.forward">(Autograd.shrinkOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.forward">(Autograd.sliceOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.subOp.forward">(Autograd.subOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.forward">(Autograd.sumOp method)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.TensorResource.forward">(Autograd.TensorResource method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Conv2D.forward">(Network.Conv2D method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Dropout.forward">(Network.Dropout method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Flatten.forward">(Network.Flatten method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Linear.forward">(Network.Linear method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.MaxPool.forward">(Network.MaxPool method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.forward">(Network.Module method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Net.forward">(Network.Net method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.ReLU.forward">(Network.ReLU method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Sequential.forward">(Network.Sequential method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Softmax.forward">(Network.Softmax method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.fromRanges">fromRanges() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.fromShape">fromShape() (StaticTensor.tensor method)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.fullOuter">fullOuter() (NDArray.ndarray method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Remote.html#Remote.Remote.get">get() (Remote.Remote method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Remote.html#Remote.remote.get">(Remote.remote method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.getFirstIdx">getFirstIdx() (in module NDArray)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes.getInt">getInt() (Network.moduleAttributes method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.getRank">getRank() (in module Autograd)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Module.getSubModuleName">getSubModuleName() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.grad">grad() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.grad">(DynamicTensor.Tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.gradData">gradData() (Autograd.BaseTensorResource method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.gradResource">gradResource (Autograd.BaseTensorResource attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.hardCheckRank">hardCheckRank() (DynamicTensor.Tensor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.helpFindModuleByName">helpFindModuleByName() (in module Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.helpFindParamDataByName">helpFindParamDataByName() (in module Network)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.imageType">imageType() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.indexAt">indexAt() (in module Utilities)</a>, <a href="modules/lib/Utilities.html#Utilities.indexAt">[1]</a>
+
+      <ul>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.indexAt">(Standard._domain method)</a>, <a href="modules/lib/Utilities/Standard.html#Standard._domain.indexAt">[1]</a>
+</li>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.indexAt">(Standard._tuple method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Utilities.html#Utilities.indexAtHelperMultiples">indexAtHelperMultiples() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.indexAtHelperProd">indexAtHelperProd() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.indexInShape">indexInShape() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.TensorResource.init">init() (Autograd.TensorResource method)</a>, <a href="modules/lib/Autograd.html#Autograd.TensorResource.init">[1]</a>, <a href="modules/lib/Autograd.html#Autograd.TensorResource.init">[2]</a>, <a href="modules/lib/Autograd.html#Autograd.TensorResource.init">[3]</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">(DynamicTensor.Tensor method)</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">[1]</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">[2]</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">[3]</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">[4]</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.init">[5]</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.init">(NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[1]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[2]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[3]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[4]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[5]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[6]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init">[7]</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData.init">(NDArray.NDArrayData method)</a>, <a href="modules/lib/NDArray.html#NDArray.NDArrayData.init">[1]</a>, <a href="modules/lib/NDArray.html#NDArray.NDArrayData.init">[2]</a>, <a href="modules/lib/NDArray.html#NDArray.NDArrayData.init">[3]</a>, <a href="modules/lib/NDArray.html#NDArray.NDArrayData.init">[4]</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.remote.init">(NDArray.remote method)</a>, <a href="modules/lib/NDArray.html#NDArray.remote.init">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Conv2D.init">(Network.Conv2D method)</a>, <a href="modules/lib/Network.html#Network.Conv2D.init">[1]</a>, <a href="modules/lib/Network.html#Network.Conv2D.init">[2]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Dropout.init">(Network.Dropout method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Flatten.init">(Network.Flatten method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Linear.init">(Network.Linear method)</a>, <a href="modules/lib/Network.html#Network.Linear.init">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.MaxPool.init">(Network.MaxPool method)</a>, <a href="modules/lib/Network.html#Network.MaxPool.init">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.init">(Network.Module method)</a>, <a href="modules/lib/Network.html#Network.Module.init">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.moduleAttributes.init">(Network.moduleAttributes method)</a>, <a href="modules/lib/Network.html#Network.moduleAttributes.init">[1]</a>, <a href="modules/lib/Network.html#Network.moduleAttributes.init">[2]</a>, <a href="modules/lib/Network.html#Network.moduleAttributes.init">[3]</a>, <a href="modules/lib/Network.html#Network.moduleAttributes.init">[4]</a>, <a href="modules/lib/Network.html#Network.moduleAttributes.init">[5]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.moduleChildren.init">(Network.moduleChildren method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Net.init">(Network.Net method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Parameter.init">(Network.Parameter method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.ReLU.init">(Network.ReLU method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Sequential.init">(Network.Sequential method)</a>, <a href="modules/lib/Network.html#Network.Sequential.init">[1]</a>, <a href="modules/lib/Network.html#Network.Sequential.init">[2]</a>, <a href="modules/lib/Network.html#Network.Sequential.init">[3]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Softmax.init">(Network.Softmax method)</a>
+</li>
+        <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.init">(OrderedDict.dict method)</a>, <a href="modules/lib/OrderedDict.html#OrderedDict.dict.init">[1]</a>, <a href="modules/lib/OrderedDict.html#OrderedDict.dict.init">[2]</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.Remote.init">(Remote.Remote method)</a>, <a href="modules/lib/Remote.html#Remote.Remote.init">[1]</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.remote.init">(Remote.remote method)</a>, <a href="modules/lib/Remote.html#Remote.remote.init">[1]</a>
+</li>
+        <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect.init">(SimpleDomain.rect method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">[1]</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">[2]</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">[3]</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">[4]</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.init">[5]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.init=">init=() (NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init=">[1]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.init=">[2]</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.remote.init=">(NDArray.remote method)</a>, <a href="modules/lib/NDArray.html#NDArray.remote.init=">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.expandOp.input">input (Autograd.expandOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.expOp.input">(Autograd.expOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.input">(Autograd.maxOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.input">(Autograd.padOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.input">(Autograd.permuteOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reluOp.input">(Autograd.reluOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.input">(Autograd.reshapeOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.input">(Autograd.shrinkOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.input">(Autograd.sliceOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.input">(Autograd.sumOp attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.insert">insert() (OrderedDict.dict method)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.insertIdx">insertIdx() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.isNormal">isNormal() (Standard._domain method)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.Remote.item">item (Remote.Remote attribute)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.items">items() (Network.moduleChildren iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.ith">ith() (Network.moduleChildren method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.kernel">kernel (Autograd.conv2DOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Conv2D.kernel">(Network.Conv2D attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.kernelRot">kernelRot() (NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.kernelRot">[1]</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Conv2D.kernelShape">kernelShape (Network.Conv2D attribute)</a>
+</li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.keys">keys() (OrderedDict.dict iterator)</a>
+</li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.keyType">keyType (OrderedDict.dict attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp">layerSliceOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes.layerType">layerType (Network.moduleAttributes attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.ModuleSpecification.layerType">(Network.ModuleSpecification attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.lhs">lhs (Autograd.addOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.divOp.lhs">(Autograd.divOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.lhs">(Autograd.multOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.subOp.lhs">(Autograd.subOp attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Linear">Linear (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.load">load() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.loadFromNumpy">loadFromNumpy() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/LoadNumpy.html#module-LoadNumpy">LoadNumpy (module)</a>
+</li>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.loadNumpyArray">loadNumpyArray() (in module LoadNumpy)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.loadPyTorchDump">loadPyTorchDump() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.remote.localAccess">localAccess() (Remote.remote method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.loopGpuSupport">loopGpuSupport (in module Utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Linear.m">m (Network.Linear attribute)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.main">main() (in module DynamicTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.main">(in module LoadNumpy)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.main">(in module NDArray)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.main">(in module Network)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.main">(in module StaticTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.map">map() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.mask">mask (Autograd.layerSliceOp attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.maskDomain">maskDomain (Autograd.layerSliceOp attribute)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.matvec">matvec() (in module StaticTensor)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.matvec">[1]</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.matvecmul">matvecmul() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.matvecmul">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.matvecmul">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.matvecmulFast">matvecmulFast() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.matvecmulFast">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.max">max() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.max">(NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.max">[1]</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.max">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.maxOp">maxOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.MaxPool">MaxPool (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.maxPool">maxPool() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.maxPool">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.maxPool">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.maxOp.maxRank">maxRank (Autograd.maxOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.maxRank">(in module DynamicTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.Sequential.mds">mds (Network.Sequential attribute)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.meta">meta (DynamicTensor.Tensor attribute)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.meta">meta() (StaticTensor.tensor method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.mod">mod() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.modelFromSpecFile">modelFromSpecFile() (in module Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module">Module (class in Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes">moduleAttributes (record in Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren">moduleChildren (record in Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.class.moduleFieldNames">moduleFieldNames() (Network.class iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.class.moduleFields">moduleFields() (Network.class iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleFromSpec">moduleFromSpec() (in module Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.moduleId">moduleId (Network.Module attribute)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleInstances">moduleInstances (in module Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.moduleName">moduleName (Network.Module attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.moduleAttributes.moduleName">(Network.moduleAttributes attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.Module.moduleNames">moduleNames() (Network.Module iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.modules">modules() (Network.Module iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.ModuleSpecification">ModuleSpecification (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.multiReader">multiReader() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.multOp">multOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.myShape">myShape() (Standard._domain method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.n">n (in module StaticTensor)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Linear.n">(Network.Linear attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.Module.namedModules">namedModules() (Network.Module iterator)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.nbase">nbase() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#module-NDArray">NDArray (module)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray">ndarray (record in NDArray)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData">NDArrayData (class in NDArray)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Net">Net (class in Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#module-Network">Network (module)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.newRank">newRank (Autograd.reshapeOp attribute)</a>
+</li>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.NoRankArray">NoRankArray (class in LoadNumpy)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._domain.normalize">normalize() (Standard._domain method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.normalizeArray">normalizeArray() (in module Utilities)</a>, <a href="modules/lib/Utilities.html#Utilities.normalizeArray">[1]</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.normalizeDomain">normalizeDomain() (in module Utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect.offset">offset (SimpleDomain.rect attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.oldRank">oldRank (Autograd.reshapeOp attribute)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.ones">ones() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.ones">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.ones">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.TensorResource.operation">operation (Autograd.TensorResource attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.TensorResource.operationData">operationData (Autograd.TensorResource attribute)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.order">order (Network.moduleChildren attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.order">(OrderedDict.dict attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/OrderedDict.html#module-OrderedDict">OrderedDict (module)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.sumOp.outRank">outRank() (Autograd.sumOp method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.ownedModules">ownedModules (Network.Module attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.pad">pad() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.pad">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.padOp">padOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.par">par() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Parameter">Parameter (class in Network)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.parameterNames">parameterNames() (Network.Module iterator)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.parameters">parameters() (Network.Module iterator)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.permutation">permutation (Autograd.permuteOp attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.permute">permute() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.permute">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.permuteOp">permuteOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.MaxPool.poolSize">poolSize (Network.MaxPool attribute)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.populateRemote">populateRemote() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.class.postinit">postinit() (Network.class method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes.prettyPrint">prettyPrint() (Network.moduleAttributes method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleAttributes.prettyPrintSpec">prettyPrintSpec() (Network.moduleAttributes method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities.html#Utilities.rangeFromBound">rangeFromBound() (in module Utilities)</a>, <a href="modules/lib/Utilities.html#Utilities.rangeFromBound">[1]</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.rangeFromStart">rangeFromStart() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.rangesFromBounds">rangesFromBounds() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#Utilities.rangesFromShape">rangesFromShape() (in module Utilities)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.rank">rank (Autograd.addOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.rank">(Autograd.BaseTensorResource attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.expandOp.rank">(Autograd.expandOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.ForgetfulTensor.rank">(Autograd.ForgetfulTensor attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.layerSliceOp.rank">(Autograd.layerSliceOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.maxOp.rank">(Autograd.maxOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.rank">(Autograd.multOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.padOp.rank">(Autograd.padOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.permuteOp.rank">(Autograd.permuteOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp.rank">(Autograd.shrinkOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sliceOp.rank">(Autograd.sliceOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.sumOp.rank">(Autograd.sumOp attribute)</a>
+</li>
+        <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.ArrClass.rank">(LoadNumpy.ArrClass attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray._tensor_resource.rank">(NDArray._tensor_resource attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.rank">(NDArray.ndarray attribute)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.NDArrayData.rank">(NDArray.NDArrayData attribute)</a>
+</li>
+        <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect.rank">(SimpleDomain.rect attribute)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.rank">(StaticTensor.tensor attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.read">read() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.read">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.readData">readData() (in module LoadNumpy)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.readInPlace">readInPlace() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/LoadNumpy.html#LoadNumpy.readShape">readShape() (in module LoadNumpy)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect">rect (record in SimpleDomain)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.class.registerModules">registerModules() (Network.class method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.ReLU">ReLU (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.relu">relu() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.relu">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.relu">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reluOp">reluOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.Remote">Remote (class in Remote)</a>
+
+      <ul>
+        <li><a href="modules/lib/Remote.html#module-Remote">(module)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Remote.html#Remote.remote">remote (record in Remote)</a>
+</li>
+      <li><a href="modules/lib/Remote.html#Remote.remote.remoteResource">remoteResource (Remote.remote attribute)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.removeIdx">removeIdx() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.reshape">reshape() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.reshape">(NDArray.ndarray method)</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.reshape">[1]</a>, <a href="modules/lib/NDArray.html#NDArray.ndarray.reshape">[2]</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.reshape">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.reshape">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.reshapeDomain">reshapeDomain() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.reshapeDomain">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp">reshapeOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.resource">resource (StaticTensor.tensor attribute)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.resource">resource() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.resource">(NDArray.ndarray method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.addOp.rhs">rhs (Autograd.addOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.divOp.rhs">(Autograd.divOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.multOp.rhs">(Autograd.multOp attribute)</a>
+</li>
+        <li><a href="modules/lib/Autograd.html#Autograd.subOp.rhs">(Autograd.subOp attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.runtimeRank">runtimeRank (DynamicTensor.Tensor attribute)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.runtimeRank">runtimeRank() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Autograd.html#Autograd.TensorEssence.runtimeRank">(Autograd.TensorEssence method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.save">save() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Sequential">Sequential (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.serialize">serialize() (DynamicTensor.Tensor method)</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.serialize">[1]</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.serialize">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.Remote.serialize">(Remote.Remote method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.serialize">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.serialize">[1]</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.serialize">[2]</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.setData">setData() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Conv2D.setup">setup() (Network.Conv2D method)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Linear.setup">(Network.Linear method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.setup">(Network.Module method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.reshapeOp.shape">shape (Autograd.reshapeOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect.shape">(SimpleDomain.rect attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.shrink">shrink() (NDArray.ndarray method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.shrink">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.shrinkOp">shrinkOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.signature">signature() (Network.Module method)</a>
+</li>
+      <li><a href="modules/lib/SimpleDomain.html#module-SimpleDomain">SimpleDomain (module)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.size">size (in module StaticTensor)</a>
+</li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.size">size() (OrderedDict.dict method)</a>
+
+      <ul>
+        <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.rect.size">(SimpleDomain.rect method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.slice">slice() (DynamicTensor.Tensor method)</a>, <a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.slice">[1]</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.slice">(in module NDArray)</a>
+</li>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.slice">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.slice">(Standard._tuple method)</a>, <a href="modules/lib/Utilities/Standard.html#Standard._tuple.slice">[1]</a>
+</li>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard.string.slice">(Standard.string method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.slice">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.slice">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/NDArray.html#NDArray.sliceGeneric">sliceGeneric() (in module NDArray)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.sliceOp">sliceOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Softmax">Softmax (class in Network)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.softmax">softmax() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.softmax">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.splitAt">splitAt() (in module NDArray)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.squeeze">squeeze() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Standard.html#module-Standard">Standard (module)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#module-StaticTensor">StaticTensor (module)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Types.html#Types.stdRange">stdRange (in module Types)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.conv2DOp.stride">stride (Autograd.conv2DOp attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.Conv2D.stride">(Network.Conv2D attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Network.html#Network.ModuleSpecification.subModuleOrder">subModuleOrder (Network.ModuleSpecification attribute)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.Module.subModules">subModules (Network.Module attribute)</a>
+
+      <ul>
+        <li><a href="modules/lib/Network.html#Network.ModuleSpecification.subModules">(Network.ModuleSpecification attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Autograd.html#Autograd.subOp">subOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.sum">sum() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.sum">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.sum">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.sumAxesMask">sumAxesMask() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.sumOneAxis">sumOneAxis() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.sumOp">sumOp (record in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.sumOp.sumRank">sumRank (Autograd.sumOp attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.table">table (OrderedDict.dict attribute)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.take">take() (in module NDArray)</a>
+
+      <ul>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard.string.take">(Standard.string method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard.string.takeUntil">takeUntil() (Standard.string method)</a>
+</li>
+      <li><a href="modules/lib/Tensor.html#module-Tensor">Tensor (module)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor">(record in DynamicTensor)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor">tensor (record in StaticTensor)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.TensorEssence">TensorEssence (class in Autograd)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensorFromCtx">tensorFromCtx() (in module StaticTensor)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.tensorize">tensorize() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/Autograd.html#Autograd.TensorResource">TensorResource (class in Autograd)</a>
+</li>
+      <li><a href="modules/lib/Network.html#Network.moduleChildren.these">these() (Network.moduleChildren iterator)</a>
+
+      <ul>
+        <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.these">(OrderedDict.dict iterator)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.this">this() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.this">(NDArray.ndarray method)</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.class.this">(Network.class method)</a>, <a href="modules/lib/Network.html#Network.class.this">[1]</a>
+</li>
+        <li><a href="modules/lib/Network.html#Network.Module.this">(Network.Module method)</a>
+</li>
+        <li><a href="modules/lib/Utilities/Standard.html#Standard.string.this">(Standard.string method)</a>
+</li>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.this">(StaticTensor.tensor method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.BaseTensorResource.to">to() (Autograd.BaseTensorResource method)</a>
+
+      <ul>
+        <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.to">(DynamicTensor.Tensor method)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.Remote.to">(Remote.Remote method)</a>
+</li>
+        <li><a href="modules/lib/Remote.html#Remote.remote.to">(Remote.remote method)</a>
+</li>
+      </ul></li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.toArray">toArray() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.toNDArray">toNDArray() (DynamicTensor.Tensor method)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.ndarray.toRemote">toRemote() (NDArray.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.ndarray.toTensor">toTensor() (DynamicTensor.ndarray method)</a>
+</li>
+      <li><a href="modules/lib/Utilities/Types.html#module-Types">Types (module)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Remote.html#Remote.remote.unload">unload() (Remote.remote method)</a>
+</li>
+      <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.unsqueeze">unsqueeze() (StaticTensor.tensor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Utilities/Standard.html#Standard._tuple.untuplify">untuplify() (Standard._tuple method)</a>
+</li>
+      <li><a href="modules/lib/Utilities.html#module-Utilities">Utilities (module)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.valType">valType (OrderedDict.dict attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Autograd.html#Autograd.padOp.value">value (Autograd.padOp attribute)</a>
+</li>
+      <li><a href="modules/lib/OrderedDict.html#OrderedDict.dict.values">values() (OrderedDict.dict iterator)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/Network.html#Network.Linear.weight">weight (Network.Linear attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.write">write() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/NDArray.html#NDArray.ndarray.write">(NDArray.ndarray method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Z">Z</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.Tensor.zeros">zeros() (DynamicTensor.Tensor method)</a>
+
+      <ul>
+        <li><a href="modules/lib/StaticTensor.html#StaticTensor.tensor.zeros">(StaticTensor.tensor method)</a>, <a href="modules/lib/StaticTensor.html#StaticTensor.tensor.zeros">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="modules/lib/SimpleDomain.html#SimpleDomain.zeroTup">zeroTup() (in module SimpleDomain)</a>
+</li>
+      <li><a href="modules/lib/NDArray.html#NDArray.zipArr">zipArr() (in module NDArray)</a>
+</li>
+      <li><a href="modules/lib/DynamicTensor.html#DynamicTensor.zipBinOp">zipBinOp() (in module DynamicTensor)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
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+           <div itemprop="articleBody">
+             
+  <section id="chpldoc-documentation">
+<h1>chpldoc documentation<a class="headerlink" href="#chpldoc-documentation" title="Link to this heading">¶</a></h1>
+<p>Contents:</p>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Autograd.html">Autograd</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="modules/lib/NDArray.html">NDArray</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Network.html">Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/OrderedDict.html">OrderedDict</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="modules/lib/StaticTensor.html">StaticTensor</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Standard.html">Standard</a></li>
+<li class="toctree-l1"><a class="reference internal" href="modules/lib/Utilities/Types.html">Types</a></li>
+</ul>
+</div>
+</section>
+<section id="indices-and-tables">
+<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
+<ul class="simple">
+<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
+<li><p><a class="reference internal" href="chpl-modindex.html"><em>Chapel Module Index</em></a></p></li>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+<li class="toctree-l1"><a class="reference internal" href="DynamicTensor.html">DynamicTensor</a></li>
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+            <a href="../../_sources/modules/lib/Autograd.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="autograd">
+<span id="module-Autograd"></span><h1>Autograd<a class="headerlink" href="#autograd" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Autograd</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Autograd</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Autograd.checkRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">checkRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">te:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(?eltType)</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">bool</span></em><a class="headerlink" href="#Autograd.checkRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Autograd.getRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">getRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">te:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(?eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.getRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Autograd.forceRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forceRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">te:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(?eltType)</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.forceRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Autograd.TensorEssence">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">TensorEssence</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.TensorEssence" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.TensorEssence.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.TensorEssence.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.TensorEssence.runtimeRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">runtimeRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.TensorEssence.runtimeRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Autograd.ForgetfulTensor">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">ForgetfulTensor</span></span><em class="property"> <span class="pre">:</span> <span class="pre">TensorEssence</span></em><a class="headerlink" href="#Autograd.ForgetfulTensor" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.ForgetfulTensor.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.ForgetfulTensor.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">BaseTensorResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">TensorEssence,</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.BaseTensorResource" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.dataResource">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">dataResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.dataResource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.gradResource">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">gradResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.gradResource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.to">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">device_:</span> <span class="pre">locale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.BaseTensorResource.to" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.device">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">device</span></span><em class="property"> <span class="pre">:</span> <span class="pre">locale</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.device" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.BaseTensorResource.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">alreadyPopulated</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.BaseTensorResource.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.array">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">array</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.array" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.data">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">data</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">[]</span> <span class="pre">eltType</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.grad">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">grad</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.grad" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.gradData">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">gradData</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.gradData" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource._loc">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">_loc</span></span><a class="headerlink" href="#Autograd.BaseTensorResource._loc" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.BaseTensorResource.runtimeRank">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">runtimeRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.BaseTensorResource.runtimeRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">TensorResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">BaseTensorResource(?),</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.TensorResource" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource.operation">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">operation</span></span><a class="headerlink" href="#Autograd.TensorResource.operation" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource.operationData">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">operationData</span></span><em class="property"> <span class="pre">:</span> <span class="pre">operation</span></em><a class="headerlink" href="#Autograd.TensorResource.operationData" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">operation</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.TensorResource.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">operationData:</span> <span class="pre">?operation</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">resource:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em>, <em class="sig-param"><span class="pre">operationData:</span> <span class="pre">?operation</span></em>, <em class="sig-param"><span class="pre">device_:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">data:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">operationData:</span> <span class="pre">?operation</span></em>, <em class="sig-param"><span class="pre">device_:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.TensorResource.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.TensorResource.backward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">alreadyPopulated:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.TensorResource.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.baseValue">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">baseValue</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.baseValue" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.baseValue.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.baseValue.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.baseValue.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.baseValue.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.reluOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">reluOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.reluOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reluOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.reluOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reluOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.reluOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reluOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.reluOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reluOp._relu">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">x</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.reluOp._relu" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.expOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">expOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.expOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.expOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.expOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.expOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.expOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.expOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.expOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.addOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">addOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.addOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.addOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.addOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.addOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><a class="headerlink" href="#Autograd.addOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.addOp.lhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">lhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.addOp.lhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.addOp.rhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">rhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.addOp.rhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.addOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.addOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.addOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.addOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.addOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.addOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.subOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">subOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.subOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.subOp.lhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">lhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.subOp.lhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.subOp.rhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">rhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.subOp.rhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.subOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.subOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.divOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">divOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.divOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.divOp.lhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">lhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.divOp.lhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.divOp.rhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">rhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?)</span></em><a class="headerlink" href="#Autograd.divOp.rhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.divOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.divOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.multOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">multOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.multOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.multOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.multOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.multOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><a class="headerlink" href="#Autograd.multOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.multOp.lhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">lhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.multOp.lhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.multOp.rhs">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">rhs</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.multOp.rhs" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.multOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.multOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.multOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.multOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.multOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.multOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">reshapeOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.reshapeOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.oldRank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">oldRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.reshapeOp.oldRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.newRank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">newRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.reshapeOp.newRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><a class="headerlink" href="#Autograd.reshapeOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.shape">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">shape</span></span><em class="property"> <span class="pre">:</span> <span class="pre">newRank*int</span></em><a class="headerlink" href="#Autograd.reshapeOp.shape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">oldRank)</span></em><a class="headerlink" href="#Autograd.reshapeOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.reshapeOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(newRank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.reshapeOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.reshapeOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(newRank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(oldRank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.reshapeOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">permuteOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.permuteOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.permuteOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.permuteOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.permutation">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">permutation</span></span><a class="headerlink" href="#Autograd.permuteOp.permutation" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.permuteOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.permuteOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.permuteOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.permuteOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.permuteOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.expandOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">expandOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.expandOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.expandOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.expandOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.expandedShape">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">expandedShape</span></span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#Autograd.expandOp.expandedShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.expandOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.expandOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.expandOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.expandOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.expandOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.padOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">padOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.padOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.padOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.padOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.padOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.padOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.padOp.arg">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">arg</span></span><em class="property"> <span class="pre">:</span> <span class="pre">rank*(2*(int))</span></em><a class="headerlink" href="#Autograd.padOp.arg" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.padOp.value">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">value</span></span><em class="property"> <span class="pre">:</span> <span class="pre">eltType</span></em><a class="headerlink" href="#Autograd.padOp.value" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.padOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.padOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.padOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.padOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.padOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.padOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.padOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.padOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">shrinkOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.shrinkOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.shrinkOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.shrinkOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.arg">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">arg</span></span><em class="property"> <span class="pre">:</span> <span class="pre">rank*(2*(int))</span></em><a class="headerlink" href="#Autograd.shrinkOp.arg" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.shrinkOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.shrinkOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.shrinkOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.shrinkOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.shrinkOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">sliceOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.sliceOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.sliceOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.sliceOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.dom">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">dom</span></span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#Autograd.sliceOp.dom" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.sliceOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.sliceOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.sliceOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sliceOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.sliceOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">layerSliceOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.layerSliceOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.layerSliceOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.layerSliceOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.base">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">base</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.layerSliceOp.base" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.mask">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">mask</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.layerSliceOp.mask" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.maskDomain">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">maskDomain</span></span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#Autograd.layerSliceOp.maskDomain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.layerSliceOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.layerSliceOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.layerSliceOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.layerSliceOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.sumOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">sumOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.sumOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.sumOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.sumOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.sumRank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">sumRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.sumOp.sumRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.axes">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">axes</span></span><em class="property"> <span class="pre">:</span> <span class="pre">sumRank*int</span></em><a class="headerlink" href="#Autograd.sumOp.axes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.sumOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.sumOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.outRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">outRank</span></span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.sumOp.outRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.sumOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.sumOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(outRank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.sumOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.maxOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">maxOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.maxOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.maxOp.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.maxOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.maxRank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">maxRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.maxOp.maxRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.axes">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">axes</span></span><em class="property"> <span class="pre">:</span> <span class="pre">maxRank*int</span></em><a class="headerlink" href="#Autograd.maxOp.axes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.input">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">input</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#Autograd.maxOp.input" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.maxOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Autograd.maxOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.maxOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(rank,</span> <span class="pre">eltType),)</span></em><a class="headerlink" href="#Autograd.maxOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">conv2DOp</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Autograd.conv2DOp" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Autograd.conv2DOp.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.features">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">features</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">3)</span></em><a class="headerlink" href="#Autograd.conv2DOp.features" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.kernel">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">kernel</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">4)</span></em><a class="headerlink" href="#Autograd.conv2DOp.kernel" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.stride">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">stride</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Autograd.conv2DOp.stride" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.children">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">children</span></span><a class="headerlink" href="#Autograd.conv2DOp.children" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(3,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#Autograd.conv2DOp.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Autograd.conv2DOp.backward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">grad:</span> <span class="pre">ndarray(3</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(ndarray(3,</span> <span class="pre">eltType),</span> <span class="pre">ndarray(4,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#Autograd.conv2DOp.backward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../../index.html" class="btn btn-neutral float-left" title="chpldoc documentation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>DynamicTensor &mdash; chpldoc 0.0.1 documentation</title>
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+             
+  <section id="dynamictensor">
+<span id="module-DynamicTensor"></span><h1>DynamicTensor<a class="headerlink" href="#dynamictensor" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">DynamicTensor</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">DynamicTensor</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="DynamicTensor.maxRank">
+<em class="property"><span class="pre">config</span> <span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">maxRank</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">6</span></em><a class="headerlink" href="#DynamicTensor.maxRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">Tensor</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#DynamicTensor.Tensor" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#DynamicTensor.Tensor.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.meta">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">meta</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.meta" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.runtimeRank">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">runtimeRank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">-1</span></em><a class="headerlink" href="#DynamicTensor.Tensor.runtimeRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">meta:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">meta:</span> <span class="pre">shared</span> <span class="pre">TensorEssence(?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">t:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.this">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.tensorize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">tensorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.tensorize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.resource">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">resource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.resource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.forceRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forceRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.forceRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.forceRankMeta">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forceRankMeta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.forceRankMeta" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.hardCheckRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">hardCheckRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">bool</span></em><a class="headerlink" href="#DynamicTensor.Tensor.hardCheckRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.checkRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">checkRank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">bool</span></em><a class="headerlink" href="#DynamicTensor.Tensor.checkRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.to">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">device:</span> <span class="pre">locale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.to" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.device">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">device</span></span><em class="property"> <span class="pre">:</span> <span class="pre">locale</span></em><a class="headerlink" href="#DynamicTensor.Tensor.device" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.array">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.array" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.grad">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.grad" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.data">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">[]</span> <span class="pre">eltType</span></em><a class="headerlink" href="#DynamicTensor.Tensor.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.toNDArray">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">toNDArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.toNDArray" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.toArray">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">toArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">[]</span> <span class="pre">eltType</span></em><a class="headerlink" href="#DynamicTensor.Tensor.toArray" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.ndarray.toTensor">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">toTensor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.ndarray.toTensor" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.tensor.eraseRank">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">eraseRank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.tensor.eraseRank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.:">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">:</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">t:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">T:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.:" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.zipBinOp">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">zipBinOp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">opName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">a:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.zipBinOp" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.loadFromNumpy">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">loadFromNumpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(real)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.loadFromNumpy" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.+">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">+</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.+" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.-">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">-</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.-" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.*">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">*</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.*" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.sum">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...?r</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.sum" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.relu">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.relu" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.max">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.max" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.exp">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.exp" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.softmax">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.softmax" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.maxPool">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">maxPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">poolSize:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.maxPool" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.reshape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.reshape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.slice">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">rngs:</span> <span class="pre">range</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.flatten">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">flatten</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.flatten" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.matvecmul">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">matvecmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">m:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">v:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.matvecmul" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.matvecmulFast">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">matvecmulFast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">m:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">v:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.matvecmulFast" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.argmax">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">argmax</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#DynamicTensor.Tensor.argmax" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.convolve">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">Tensor(eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.convolve" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">Tensor(?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">Tensor(eltType)</span></em>, <em class="sig-param"><span class="pre">bias:</span> <span class="pre">Tensor(eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.arange">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">arange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.arange" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.ones">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.ones" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.zeros">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.zeros" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="DynamicTensor.main">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.main" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.serialize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(locking</span> <span class="pre">=</span> <span class="pre">false</span></em>, <em class="sig-param"><span class="pre">IO.defaultSerializer)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">IO.defaultSerializer</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.serialize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(?)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">?srt2</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">srt2</span> <span class="pre">!=</span> <span class="pre">IO.defaultSerializer</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.write">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fw:</span> <span class="pre">IO.fileWriter(?)</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">throws</span></em><a class="headerlink" href="#DynamicTensor.Tensor.write" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.save">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.save" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.multiReader">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">multiReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><a class="headerlink" href="#DynamicTensor.Tensor.multiReader" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.load">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">precision</span> <span class="pre">=</span> <span class="pre">64</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(real)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.load" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="DynamicTensor.Tensor.readInPlace">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">readInPlace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fr:</span> <span class="pre">IO.fileReader(?)</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">precision</span> <span class="pre">=</span> <span class="pre">64</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(real)</span></em><a class="headerlink" href="#DynamicTensor.Tensor.readInPlace" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
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+             
+  <section id="loadnumpy">
+<span id="module-LoadNumpy"></span><h1>LoadNumpy<a class="headerlink" href="#loadnumpy" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">LoadNumpy</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">LoadNumpy</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="LoadNumpy.NoRankArray">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">NoRankArray</span></span><a class="headerlink" href="#LoadNumpy.NoRankArray" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="LoadNumpy.ArrClass">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">ArrClass</span></span><em class="property"> <span class="pre">:</span> <span class="pre">NoRankArray</span></em><a class="headerlink" href="#LoadNumpy.ArrClass" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="LoadNumpy.ArrClass.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#LoadNumpy.ArrClass.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="LoadNumpy.ArrClass._domain">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">_domain</span></span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#LoadNumpy.ArrClass._domain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="LoadNumpy.ArrClass.data">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"> <span class="pre">:</span> <span class="pre">[_domain]</span> <span class="pre">real</span></em><a class="headerlink" href="#LoadNumpy.ArrClass.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="LoadNumpy.readData">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">readData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">fr</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">rank*int</span></em>, <em class="sig-param"><span class="pre">dtype:</span> <span class="pre">bytes</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">NoRankArray</span></em><a class="headerlink" href="#LoadNumpy.readData" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="LoadNumpy.readShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">readShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fr</span></em><span class="sig-paren">)</span><a class="headerlink" href="#LoadNumpy.readShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="LoadNumpy.loadNumpyArray">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">loadNumpyArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">NoRankArray</span></em><a class="headerlink" href="#LoadNumpy.loadNumpyArray" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="LoadNumpy.main">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#LoadNumpy.main" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
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+             
+  <section id="ndarray">
+<span id="module-NDArray"></span><h1>NDArray<a class="headerlink" href="#ndarray" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">NDArray</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">NDArray</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">NDArrayData</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#NDArray.NDArrayData" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#NDArray.NDArrayData.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real(64)</span></em><a class="headerlink" href="#NDArray.NDArrayData.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData._domain">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">_domain</span></span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span> <span class="pre">=</span> <span class="pre">util.emptyDomain(rank)</span></em><a class="headerlink" href="#NDArray.NDArrayData._domain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData.data">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"> <span class="pre">:</span> <span class="pre">[_domain]</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">noinit</span></em><a class="headerlink" href="#NDArray.NDArrayData.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.NDArrayData.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.NDArrayData.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(rank</span></em>, <em class="sig-param"><span class="pre">int)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(rank</span></em>, <em class="sig-param"><span class="pre">int)</span></em>, <em class="sig-param"><span class="pre">A:</span> <span class="pre">[dom]</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">A:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">me:</span> <span class="pre">NDArrayData(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="NDArray.ndarray">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">ndarray</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#NDArray.ndarray" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#NDArray.ndarray.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real(64)</span></em><a class="headerlink" href="#NDArray.ndarray.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.arrayResource">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">arrayResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">NDArrayData(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.arrayResource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.borrowResource">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">borrowResource</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">NDArrayData(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.borrowResource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.resource">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">resource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">NDArrayData(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.resource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.data">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">data</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">arrayResource.data.type</span></em><a class="headerlink" href="#NDArray.ndarray.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real(64)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">?t</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real(64)</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">isDomainType(t)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real(64)</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">isNormal:</span> <span class="pre">bool</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">isNormal</span> <span class="pre">==</span> <span class="pre">true</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">isNormal:</span> <span class="pre">bool</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">isNormal</span> <span class="pre">==</span> <span class="pre">false</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">A:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">it:</span> <span class="pre">_iteratorRecord</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.init=">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">other:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.init=" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">other:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">other:</span> <span class="pre">_iteratorRecord</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.this">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args:</span> <span class="pre">int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#NDArray.ndarray.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.setData">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">setData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">arr.rank</span> <span class="pre">==</span> <span class="pre">rank</span></em><a class="headerlink" href="#NDArray.ndarray.setData" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.reshapeDomain">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">reshapeDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">arrayResource._domain.type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.reshapeDomain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.reshape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">arrayResource._domain.type</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.reshape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">?t</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(dom.rank,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isDomainType(t)</span> <span class="pre">&amp;&amp;</span> <span class="pre">dom.rank</span> <span class="pre">!=</span> <span class="pre">rank</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">newShape:</span> <span class="pre">int</span> <span class="pre">...?newRank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(newRank,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.slice">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.permute">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.permute" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.expand">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.expand" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.sumOneAxis">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">sumOneAxis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axis:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.sumOneAxis" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.sumAxesMask">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">sumAxesMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">withAxesMask:</span> <span class="pre">rank*int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.sumAxesMask" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.sum">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...?axesCount</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.sum" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.shrink">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">shrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">narg:</span> <span class="pre">2*int</span> <span class="pre">...rank</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">exactBounds</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.shrink" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.pad">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">narg:</span> <span class="pre">2*int</span> <span class="pre">...rank</span></em>, <em class="sig-param"><span class="pre">value:</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">0</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.pad" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.dilate">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">dilate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dil:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">2</span></em><a class="headerlink" href="#NDArray.ndarray.dilate" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">dilate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dil:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">3</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.squeeze">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">squeeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">newRank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(newRank,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">newRank</span> <span class="pre">&lt;</span> <span class="pre">rank</span></em><a class="headerlink" href="#NDArray.ndarray.squeeze" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.max">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.max" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...?axesCount</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.populateRemote">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">populateRemote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">re:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#NDArray.ndarray.populateRemote" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.toRemote">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">toRemote</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#NDArray.ndarray.toRemote" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.batchify">
+<em class="property"><span class="pre">iter</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">batchify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">dim:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">dim</span> <span class="pre">&lt;</span> <span class="pre">rank</span></em><a class="headerlink" href="#NDArray.ndarray.batchify" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.kernelRot">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">kernelRot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(4,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">4</span></em><a class="headerlink" href="#NDArray.ndarray.kernelRot" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">kernelRot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(3,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">3</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.argmax">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">argmax</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em><a class="headerlink" href="#NDArray.ndarray.argmax" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.relu">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.relu" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.arange">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">arange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">to:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real(64)</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">?rank*int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.arange" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.=">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">lhs:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">rhs:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.=" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">lhs:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">rhs:</span> <span class="pre">[?d]</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">d.rank</span> <span class="pre">==</span> <span class="pre">rank</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.:">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">:</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">val:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">t:</span> <span class="pre">ndarray(val.rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.:" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.remote.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">remote.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">other:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.remote.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.remote.init=">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">remote.</span></span><span class="sig-name descname"><span class="pre">init=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">other:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.remote.init=" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">lhs:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em>, <em class="sig-param"><span class="pre">rhs:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">:</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">val:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">t:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">remote.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">other:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">remote.</span></span><span class="sig-name descname"><span class="pre">init=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">other:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">=</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ref</span> <span class="pre">lhs:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em>, <em class="sig-param"><span class="pre">rhs:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.zipArr">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">zipArr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">f</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.zipArr" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.+">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">+</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.+" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.*">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">*</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.*" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.-">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">-</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.-" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray./">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">/</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray./" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">+</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">remote(ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType))</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">remote(ndarray(rank</span></em>, <em class="sig-param"><span class="pre">eltType))</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.convolve">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">ndarray(3</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">ndarray(4</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(3,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.convolve" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">ndarray(3</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">ndarray(4</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">bias:</span> <span class="pre">ndarray(1</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(3,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.maxPool">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">maxPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">ndarray(3</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">poolSize:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(3,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.maxPool" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.matvecmul">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">matvecmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">mat:</span> <span class="pre">ndarray(2</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">vec:</span> <span class="pre">ndarray(1</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.matvecmul" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.fromRanges">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">fromRanges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">rngs:</span> <span class="pre">range</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.fromRanges" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.serialize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(locking</span> <span class="pre">=</span> <span class="pre">false</span></em>, <em class="sig-param"><span class="pre">IO.defaultSerializer)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">IO.defaultSerializer</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.serialize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.read">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fr:</span> <span class="pre">IO.fileReader(?)</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">throws</span></em><a class="headerlink" href="#NDArray.ndarray.read" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.write">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fw:</span> <span class="pre">IO.fileWriter(?)</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">throws</span></em><a class="headerlink" href="#NDArray.ndarray.write" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="NDArray._tensor_resource">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">_tensor_resource</span></span><a class="headerlink" href="#NDArray._tensor_resource" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray._tensor_resource.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#NDArray._tensor_resource.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray._tensor_resource.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real(64)</span></em><a class="headerlink" href="#NDArray._tensor_resource.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="NDArray._tensor_resource.data">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"> <span class="pre">:</span> <span class="pre">remote(ndarray(rank,</span> <span class="pre">eltType))</span></em><a class="headerlink" href="#NDArray._tensor_resource.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.fullOuter">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">fullOuter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rankA</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(?rankB</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">ndarray(rankA+rankB,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#NDArray.ndarray.fullOuter" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.contract">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">contract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rankA</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(?rankB</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">axisA:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">axisB:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.contract" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.splitAt">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">splitAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">del:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.splitAt" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.getFirstIdx">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">getFirstIdx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">del:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.getFirstIdx" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.sliceGeneric">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">sliceGeneric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">t</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">stop:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">t</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">start</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.sliceGeneric" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.slice">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">stop:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">start</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.take">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">take</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">count:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.take" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.drop">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">drop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">count:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">s:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#NDArray.drop" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="NDArray.ndarray.einsum">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">ndarray.</span></span><span class="sig-name descname"><span class="pre">einsum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">subscripts:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">a:</span> <span class="pre">ndarray(?rankA</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">ndarray(?rankB</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.ndarray.einsum" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="NDArray.main">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#NDArray.main" title="Link to this definition">¶</a></dt>
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+             
+  <section id="network">
+<span id="module-Network"></span><h1>Network<a class="headerlink" href="#network" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Network</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Network</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.helpFindModuleByName">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">helpFindModuleByName</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arg</span></em>, <em class="sig-param"><span class="pre">x:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(?)?</span></em><a class="headerlink" href="#Network.helpFindModuleByName" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.helpFindParamDataByName">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">helpFindParamDataByName</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arg</span></em>, <em class="sig-param"><span class="pre">x:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(?)</span></em><a class="headerlink" href="#Network.helpFindParamDataByName" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Network.moduleChildren">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">moduleChildren</span></span><a class="headerlink" href="#Network.moduleChildren" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Network.moduleChildren.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.childDict">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">childDict</span></span><em class="property"> <span class="pre">:</span> <span class="pre">map(string,</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType))</span></em><a class="headerlink" href="#Network.moduleChildren.childDict" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.order">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">order</span></span><em class="property"> <span class="pre">:</span> <span class="pre">list(string)</span></em><a class="headerlink" href="#Network.moduleChildren.order" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.moduleChildren.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.these">
+<em class="property"><span class="pre">iter</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">these</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><a class="headerlink" href="#Network.moduleChildren.these" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.items">
+<em class="property"><span class="pre">iter</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(string,</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType))</span></em><a class="headerlink" href="#Network.moduleChildren.items" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.add">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">m:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.moduleChildren.add" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleChildren.ith">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">ith</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">i:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><a class="headerlink" href="#Network.moduleChildren.ith" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.class.this">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fieldName:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(?)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Module(?))</span></em><a class="headerlink" href="#Network.class.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fieldName:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(?)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Parameter(?))</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.class.moduleFieldNames">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">moduleFieldNames</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Module(?))</span></em><a class="headerlink" href="#Network.class.moduleFieldNames" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.class.moduleFields">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">moduleFields</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(string,</span> <span class="pre">borrowed</span> <span class="pre">Module(?))</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Module(?))</span></em><a class="headerlink" href="#Network.class.moduleFields" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.class.registerModules">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">registerModules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Module(?))</span></em><a class="headerlink" href="#Network.class.registerModules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.class.postinit">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">class.</span></span><span class="sig-name descname"><span class="pre">postinit</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">isSubtype(this.type,</span> <span class="pre">Module(?))</span></em><a class="headerlink" href="#Network.class.postinit" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">moduleAttributes</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Network.moduleAttributes" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.layerType">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">layerType</span></span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.moduleAttributes.layerType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.moduleName">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">moduleName</span></span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.moduleAttributes.moduleName" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.attributes">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><em class="property"> <span class="pre">:</span> <span class="pre">dict(string,</span> <span class="pre">string)</span></em><a class="headerlink" href="#Network.moduleAttributes.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">attrs:</span> <span class="pre">map(string</span></em>, <em class="sig-param"><span class="pre">string</span></em>, <em class="sig-param"><span class="pre">?)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.moduleAttributes.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">attrs:</span> <span class="pre">dict(string</span></em>, <em class="sig-param"><span class="pre">?)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">attrs:</span> <span class="pre">map(string</span></em>, <em class="sig-param"><span class="pre">?valType</span></em>, <em class="sig-param"><span class="pre">?)</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">valType</span> <span class="pre">!=</span> <span class="pre">string</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">attrs:</span> <span class="pre">map(string</span></em>, <em class="sig-param"><span class="pre">?valType</span></em>, <em class="sig-param"><span class="pre">?)</span></em>, <em class="sig-param"><span class="pre">order:</span> <span class="pre">list(string)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">attrs</span> <span class="pre">...?n</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">attrs(0)(0).type</span> <span class="pre">==</span> <span class="pre">string</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">layerType:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.getInt">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">getInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Network.moduleAttributes.getInt" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.prettyPrint">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">prettyPrint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.moduleAttributes.prettyPrint" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.prettyPrintSpec">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">prettyPrintSpec</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.moduleAttributes.prettyPrintSpec" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.moduleAttributes.:">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">:</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ma:</span> <span class="pre">moduleAttributes</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">T:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.moduleAttributes.:" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.ModuleSpecification">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">ModuleSpecification</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Network.ModuleSpecification" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.ModuleSpecification.layerType">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">layerType</span></span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.ModuleSpecification.layerType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.ModuleSpecification.attributes">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><em class="property"> <span class="pre">:</span> <span class="pre">map(string,</span> <span class="pre">string)</span></em><a class="headerlink" href="#Network.ModuleSpecification.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.ModuleSpecification.subModules">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">subModules</span></span><em class="property"> <span class="pre">:</span> <span class="pre">map(string,</span> <span class="pre">owned</span> <span class="pre">ModuleSpecification?)</span></em><a class="headerlink" href="#Network.ModuleSpecification.subModules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.ModuleSpecification.subModuleOrder">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">subModuleOrder</span></span><em class="property"> <span class="pre">:</span> <span class="pre">list(string)</span></em><a class="headerlink" href="#Network.ModuleSpecification.subModuleOrder" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.moduleFromSpec">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">moduleFromSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ms_:</span> <span class="pre">borrowed</span> <span class="pre">ModuleSpecification?</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Module(real)</span></em><a class="headerlink" href="#Network.moduleFromSpec" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.modelFromSpecFile">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">modelFromSpecFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">path:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Module(real)</span></em><a class="headerlink" href="#Network.modelFromSpecFile" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="Network.moduleInstances">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">moduleInstances</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">0</span></em><a class="headerlink" href="#Network.moduleInstances" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Module">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Module</span></span><a class="headerlink" href="#Network.Module" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Module.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real</span></em><a class="headerlink" href="#Network.Module.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Module.subModules">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">subModules</span></span><em class="property"> <span class="pre">:</span> <span class="pre">moduleChildren(eltType)</span></em><a class="headerlink" href="#Network.Module.subModules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Module.moduleId">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">moduleId</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Network.Module.moduleId" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Module.moduleName">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">moduleName</span></span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.Module.moduleName" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Module.ownedModules">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">ownedModules</span></span><em class="property"> <span class="pre">:</span> <span class="pre">list(shared</span> <span class="pre">Module(eltType))</span></em><a class="headerlink" href="#Network.Module.ownedModules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Module.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">ma:</span> <span class="pre">moduleAttributes</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.setup">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">setup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Network.Module.setup" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.this">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Module.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.getSubModuleName">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">getSubModuleName</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.Module.getSubModuleName" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.addModule">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">addModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">m:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Module.addModule" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">addModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">m:</span> <span class="pre">shared</span> <span class="pre">Module(eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">addModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">m:</span> <span class="pre">owned</span> <span class="pre">Module(eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.addParameter">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">addParameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">data:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Module.addParameter" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.forward">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Module.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.par">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">par</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">paramName:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Module.par" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.mod">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">mod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">modName:</span> <span class="pre">string</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><a class="headerlink" href="#Network.Module.mod" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.Module.parameters">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Parameter(eltType)</span></em><a class="headerlink" href="#Network.Module.parameters" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.Module.moduleNames">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">moduleNames</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.Module.moduleNames" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.Module.parameterNames">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">parameterNames</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.Module.parameterNames" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.Module.modules">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType)</span></em><a class="headerlink" href="#Network.Module.modules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Network.Module.namedModules">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">namedModules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(string,</span> <span class="pre">borrowed</span> <span class="pre">Module(eltType))</span></em><a class="headerlink" href="#Network.Module.namedModules" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.loadPyTorchDump">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">loadPyTorchDump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">modelPath:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">debug</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Module.loadPyTorchDump" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.attributes">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Module.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Module.signature">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">signature</span></span><em class="property"> <span class="pre">:</span> <span class="pre">string</span></em><a class="headerlink" href="#Network.Module.signature" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Parameter">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Parameter</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Parameter" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Parameter.data">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Parameter.data" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Parameter.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">data:</span> <span class="pre">Tensor(?eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Parameter.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Parameter.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Parameter.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Sequential">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Sequential</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Sequential" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Sequential.mds">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">mds</span></span><em class="property"> <span class="pre">:</span> <span class="pre">list(shared</span> <span class="pre">Module(eltType))</span></em><a class="headerlink" href="#Network.Sequential.mds" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Sequential.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">ms:</span> <span class="pre">dict(string</span></em>, <em class="sig-param"><span class="pre">shared</span> <span class="pre">Module(eltType))</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">overrideName</span> <span class="pre">=</span> <span class="pre">false</span></em>, <em class="sig-param"><span class="pre">moduleName:</span> <span class="pre">string</span> <span class="pre">=</span> <span class="pre">&quot;&quot;</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Sequential.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">ms</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">order:</span> <span class="pre">list(string)</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">ms:</span> <span class="pre">map(string</span></em>, <em class="sig-param"><span class="pre">owned</span> <span class="pre">Module(eltType)?)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">in</span> <span class="pre">ms:</span> <span class="pre">owned</span> <span class="pre">Module(real)?</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Sequential.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Sequential.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Sequential.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Sequential.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Linear">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Linear</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Linear" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Linear.m">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">m</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Network.Linear.m" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Linear.n">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">n</span></span><em class="property"> <span class="pre">:</span> <span class="pre">m.type</span></em><a class="headerlink" href="#Network.Linear.n" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Linear.weight">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">weight</span></span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Parameter(eltType)</span></em><a class="headerlink" href="#Network.Linear.weight" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Linear.bias">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">bias</span></span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Parameter(eltType)</span></em><a class="headerlink" href="#Network.Linear.bias" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Linear.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">m:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Linear.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Linear.setup">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">setup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Network.Linear.setup" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">m:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Linear.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Linear.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Linear.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Linear.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Conv2D">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Conv2D</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Conv2D" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Conv2D.kernelShape">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">kernelShape</span></span><em class="property"> <span class="pre">:</span> <span class="pre">4*(int)</span></em><a class="headerlink" href="#Network.Conv2D.kernelShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Conv2D.stride">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">stride</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Network.Conv2D.stride" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Conv2D.kernel">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">kernel</span></span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Parameter(eltType)</span></em><a class="headerlink" href="#Network.Conv2D.kernel" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.Conv2D.bias">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">bias</span></span><em class="property"> <span class="pre">:</span> <span class="pre">owned</span> <span class="pre">Parameter(eltType)</span></em><a class="headerlink" href="#Network.Conv2D.bias" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Conv2D.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">channels:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">features:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Conv2D.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">ma:</span> <span class="pre">moduleAttributes</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Conv2D.setup">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">setup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Network.Conv2D.setup" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">channels:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">features:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Conv2D.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Conv2D.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Conv2D.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Conv2D.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.MaxPool">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">MaxPool</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.MaxPool" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Network.MaxPool.poolSize">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">poolSize</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#Network.MaxPool.poolSize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.MaxPool.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">poolSize:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.MaxPool.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">poolSize:</span> <span class="pre">int</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.MaxPool.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.MaxPool.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.MaxPool.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.MaxPool.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Flatten">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Flatten</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Flatten" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Flatten.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Flatten.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Flatten.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Flatten.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Flatten.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Flatten.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.ReLU">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">ReLU</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.ReLU" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.ReLU.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.ReLU.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.ReLU.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.ReLU.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.ReLU.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.ReLU.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Softmax">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Softmax</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Softmax" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Softmax.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Softmax.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Softmax.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Softmax.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Softmax.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Softmax.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Dropout">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Dropout</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Dropout" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Dropout.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">freq:</span> <span class="pre">real</span> <span class="pre">=</span> <span class="pre">0.5</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Dropout.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Dropout.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Dropout.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Dropout.attributes">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">moduleAttributes</span></em><a class="headerlink" href="#Network.Dropout.attributes" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.chain">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">chain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">m:</span> <span class="pre">borrowed</span> <span class="pre">Module(?)</span></em>, <em class="sig-param"><span class="pre">modNames:</span> <span class="pre">string</span> <span class="pre">...?n</span></em>, <em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(?eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.chain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Network.Net">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Net</span></span><em class="property"> <span class="pre">:</span> <span class="pre">Module(?)</span></em><a class="headerlink" href="#Network.Net" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Net.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Network.Net.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Network.Net.forward">
+<em class="property"><span class="pre">override</span> <span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">input:</span> <span class="pre">Tensor(eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">Tensor(eltType)</span></em><a class="headerlink" href="#Network.Net.forward" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Network.main">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Network.main" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+  </div>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>OrderedDict &mdash; chpldoc 0.0.1 documentation</title>
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+      <li class="breadcrumb-item active">OrderedDict</li>
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+            <a href="../../_sources/modules/lib/OrderedDict.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="ordereddict">
+<span id="module-OrderedDict"></span><h1>OrderedDict<a class="headerlink" href="#ordereddict" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">OrderedDict</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">OrderedDict</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="OrderedDict.dict">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">dict</span></span><a class="headerlink" href="#OrderedDict.dict" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.keyType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">keyType</span></span><a class="headerlink" href="#OrderedDict.dict.keyType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.valType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">valType</span></span><a class="headerlink" href="#OrderedDict.dict.valType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.table">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">table</span></span><em class="property"> <span class="pre">:</span> <span class="pre">map(keyType,</span> <span class="pre">valType)</span></em><a class="headerlink" href="#OrderedDict.dict.table" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.order">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">order</span></span><em class="property"> <span class="pre">:</span> <span class="pre">list(keyType)</span></em><a class="headerlink" href="#OrderedDict.dict.order" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">in</span> <span class="pre">table:</span> <span class="pre">map(?keyType</span></em>, <em class="sig-param"><span class="pre">?valType)</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">order:</span> <span class="pre">list(keyType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#OrderedDict.dict.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">in</span> <span class="pre">table:</span> <span class="pre">map(?keyType</span></em>, <em class="sig-param"><span class="pre">?valType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">keyType</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">valType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.size">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">size</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#OrderedDict.dict.size" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.keys">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">keyType</span></em><a class="headerlink" href="#OrderedDict.dict.keys" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.values">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">valType</span></em><a class="headerlink" href="#OrderedDict.dict.values" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.these">
+<em class="property"><span class="pre">iter</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">these</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#OrderedDict.dict.these" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="OrderedDict.dict.insert">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">insert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">key:</span> <span class="pre">keyType</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">value:</span> <span class="pre">valType</span></em><span class="sig-paren">)</span><a class="headerlink" href="#OrderedDict.dict.insert" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Remote</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/modules/lib/Remote.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="remote">
+<span id="module-Remote"></span><h1>Remote<a class="headerlink" href="#remote" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Remote</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Remote</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="Remote.debug">
+<em class="property"><span class="pre">config</span> <span class="pre">const</span> </em><span class="sig-name descname"><span class="pre">debug</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">true</span></em><a class="headerlink" href="#Remote.debug" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="Remote.defaultDevice">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">defaultDevice</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">if</span> <span class="pre">here.gpus.size</span> <span class="pre">&gt;=</span> <span class="pre">1</span> <span class="pre">then</span> <span class="pre">here.gpus[0]</span> <span class="pre">else</span> <span class="pre">here</span></em><a class="headerlink" href="#Remote.defaultDevice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl class">
+<dt class="sig sig-object chpl" id="Remote.Remote">
+<em class="property"><span class="pre">class</span> </em><span class="sig-name descname"><span class="pre">Remote</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Remote.Remote" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Remote.Remote.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><a class="headerlink" href="#Remote.Remote.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Remote.Remote.device">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">device</span></span><em class="property"> <span class="pre">:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><a class="headerlink" href="#Remote.Remote.device" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Remote.Remote.item">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">item</span></span><em class="property"> <span class="pre">:</span> <span class="pre">_remoteVarWrapper(eltType)</span></em><a class="headerlink" href="#Remote.Remote.item" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.Remote.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">item:</span> <span class="pre">?eltType</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Remote.Remote.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.Remote.to">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">device_:</span> <span class="pre">locale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Remote.Remote.to" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.Remote.get">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#Remote.Remote.get" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.Remote.serialize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer</span></em>, <em class="sig-param"><span class="pre">serializer</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Remote.Remote.serialize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="Remote.remote">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">remote</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#Remote.remote" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Remote.remote.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><a class="headerlink" href="#Remote.remote.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="Remote.remote.remoteResource">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">remoteResource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">Remote(eltType)</span></em><a class="headerlink" href="#Remote.remote.remoteResource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Remote.remote.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">item:</span> <span class="pre">?eltType</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">locale</span> <span class="pre">=</span> <span class="pre">defaultDevice</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.device">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">device</span></span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#Remote.remote.device" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.get">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#Remote.remote.get" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.to">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">device_:</span> <span class="pre">locale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Remote.remote.to" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.copy">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">remote(eltType)</span></em><a class="headerlink" href="#Remote.remote.copy" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.access">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">access</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#Remote.remote.access" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.localAccess">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">localAccess</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">ref</span></em><a class="headerlink" href="#Remote.remote.localAccess" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.download">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">eltType</span></em><a class="headerlink" href="#Remote.remote.download" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Remote.remote.unload">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-name descname"><span class="pre">unload</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">eltType</span></em><a class="headerlink" href="#Remote.remote.unload" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="OrderedDict.html" class="btn btn-neutral float-left" title="OrderedDict" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="SimpleDomain.html" class="btn btn-neutral float-right" title="SimpleDomain" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2015.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/docs/modules/lib/SimpleDomain.html b/docs/modules/lib/SimpleDomain.html
new file mode 100644
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--- /dev/null
+++ b/docs/modules/lib/SimpleDomain.html
@@ -0,0 +1,172 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>SimpleDomain &mdash; chpldoc 0.0.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+<li class="toctree-l1"><a class="reference internal" href="DynamicTensor.html">DynamicTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="LoadNumpy.html">LoadNumpy</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="Remote.html">Remote</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">SimpleDomain</a></li>
+<li class="toctree-l1"><a class="reference internal" href="StaticTensor.html">StaticTensor</a></li>
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+      <li class="breadcrumb-item active">SimpleDomain</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/modules/lib/SimpleDomain.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="simpledomain">
+<span id="module-SimpleDomain"></span><h1>SimpleDomain<a class="headerlink" href="#simpledomain" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">SimpleDomain</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">SimpleDomain</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="SimpleDomain.zeroTup">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">zeroTup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#SimpleDomain.zeroTup" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">rect</span></span><a class="headerlink" href="#SimpleDomain.rect" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#SimpleDomain.rect.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect.shape">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">shape</span></span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#SimpleDomain.rect.shape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect.offset">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">offset</span></span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span> <span class="pre">=</span> <span class="pre">zeroTup(rank)</span></em><a class="headerlink" href="#SimpleDomain.rect.offset" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#SimpleDomain.rect.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="SimpleDomain.rect.size">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">size</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#SimpleDomain.rect.size" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="Remote.html" class="btn btn-neutral float-left" title="Remote" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2015.</p>
+  </div>
+
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+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">StaticTensor</a></li>
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+             
+  <section id="statictensor">
+<span id="module-StaticTensor"></span><h1>StaticTensor<a class="headerlink" href="#statictensor" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">StaticTensor</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">StaticTensor</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl record">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor">
+<em class="property"><span class="pre">record</span> </em><span class="sig-name descname"><span class="pre">tensor</span></span><em class="property"> <span class="pre">:</span> <span class="pre">serializable</span></em><a class="headerlink" href="#StaticTensor.tensor" title="Link to this definition">¶</a></dt>
+<dd><dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.rank">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">rank</span></span><em class="property"> <span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#StaticTensor.tensor.rank" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.eltType">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">real(64)</span></em><a class="headerlink" href="#StaticTensor.tensor.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl attribute">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.resource">
+<em class="property"><span class="pre">var</span> </em><span class="sig-name descname"><span class="pre">resource</span></span><em class="property"> <span class="pre">:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(eltType,</span> <span class="pre">rank)</span></em><a class="headerlink" href="#StaticTensor.tensor.resource" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.meta">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">meta</span></span><a class="headerlink" href="#StaticTensor.tensor.meta" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor._dom">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">_dom</span></span><a class="headerlink" href="#StaticTensor.tensor._dom" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.init">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real(64)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.init" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">resource:</span> <span class="pre">shared</span> <span class="pre">BaseTensorResource(?eltType</span></em>, <em class="sig-param"><span class="pre">?rank)</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">strict:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">nda:</span> <span class="pre">ndarray(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(?)</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span> <span class="pre">?eltType</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">it:</span> <span class="pre">_iteratorRecord</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.this">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.reshapeDomain">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">reshapeDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">this._dom.type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.reshapeDomain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor._setArrayData">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">_setArrayData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor._setArrayData" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.tensorFromCtx">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">tensorFromCtx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">ctx</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensorFromCtx" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.+">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">+</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">tensor(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.+" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.-">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">-</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">tensor(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.-" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.*">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">*</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">tensor(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.*" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor./">
+<em class="property"><span class="pre">operator</span> </em><span class="sig-name descname"><span class="pre">/</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a:</span> <span class="pre">tensor(?rank</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">b:</span> <span class="pre">tensor(rank</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor./" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.reshape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.reshape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">newShape:</span> <span class="pre">int</span> <span class="pre">...?newRank</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.relu">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.relu" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.permute">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.permute" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.expand">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.expand" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.pad">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args:</span> <span class="pre">2*int</span> <span class="pre">...rank</span></em>, <em class="sig-param"><span class="pre">value:</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">0.0</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.pad" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.shrink">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">shrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args:</span> <span class="pre">2*int</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.shrink" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.slice">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">dom.rank</span> <span class="pre">==</span> <span class="pre">rank</span></em><a class="headerlink" href="#StaticTensor.tensor.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">rngs:</span> <span class="pre">range</span> <span class="pre">...rank</span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.sum">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">axes:</span> <span class="pre">int</span> <span class="pre">...?r</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.sum" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.unsqueeze">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">unsqueeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dim:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank+1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.unsqueeze" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.max">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.max" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.exp">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.exp" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.softmax">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.softmax" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.matvec">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">matvec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">mat:</span> <span class="pre">tensor(2</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">vec:</span> <span class="pre">tensor(1</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.matvec" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">matvec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">mat:</span> <span class="pre">tensor(2</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">vec:</span> <span class="pre">tensor(2</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(2,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.matvecmul">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">matvecmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">m</span></em>, <em class="sig-param"><span class="pre">v</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.matvecmul" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.convolve">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">tensor(3</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">tensor(4</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(3,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.convolve" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">convolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">features:</span> <span class="pre">tensor(3</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">kernel:</span> <span class="pre">tensor(4</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">bias:</span> <span class="pre">tensor(1</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em>, <em class="sig-param"><span class="pre">stride:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(3,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.matvecmulFast">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">matvecmulFast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">mat:</span> <span class="pre">tensor(2</span></em>, <em class="sig-param"><span class="pre">?eltType)</span></em>, <em class="sig-param"><span class="pre">vec:</span> <span class="pre">tensor(1</span></em>, <em class="sig-param"><span class="pre">eltType)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(1,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.matvecmulFast" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.dilate">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">dilate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dil:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(3,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">this.rank</span> <span class="pre">==</span> <span class="pre">3</span></em><a class="headerlink" href="#StaticTensor.tensor.dilate" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.maxPool">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">maxPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">poolSize:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(3,</span> <span class="pre">eltType)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">this.rank</span> <span class="pre">==</span> <span class="pre">3</span></em><a class="headerlink" href="#StaticTensor.tensor.maxPool" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.arange">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">arange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">to:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">?rank*int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.arange" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">arange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">real)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.fromShape">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">fromShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">real</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em>, <em class="sig-param"><span class="pre">value:</span> <span class="pre">eltType</span> <span class="pre">=</span> <span class="pre">0:</span> <span class="pre">eltType</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em><a class="headerlink" href="#StaticTensor.tensor.fromShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.zeros">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">real)</span></em><a class="headerlink" href="#StaticTensor.tensor.zeros" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.ones">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">real)</span></em><a class="headerlink" href="#StaticTensor.tensor.ones" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> <span class="pre">type</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">type</span> <span class="pre">eltType</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">tensor(rank,</span> <span class="pre">eltType)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="StaticTensor.n">
+<em class="property"><span class="pre">config</span> <span class="pre">const</span> </em><span class="sig-name descname"><span class="pre">n</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">100</span></em><a class="headerlink" href="#StaticTensor.n" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="StaticTensor.diag">
+<em class="property"><span class="pre">config</span> <span class="pre">const</span> </em><span class="sig-name descname"><span class="pre">diag</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">false</span></em><a class="headerlink" href="#StaticTensor.diag" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="StaticTensor.size">
+<em class="property"><span class="pre">config</span> <span class="pre">const</span> </em><span class="sig-name descname"><span class="pre">size</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">3</span></em><a class="headerlink" href="#StaticTensor.size" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="StaticTensor.main">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.main" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.serialize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(locking</span> <span class="pre">=</span> <span class="pre">false</span></em>, <em class="sig-param"><span class="pre">IO.defaultSerializer)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">IO.defaultSerializer</span></em><span class="sig-paren">)</span><a class="headerlink" href="#StaticTensor.tensor.serialize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(?)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">?srt2</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">srt2</span> <span class="pre">!=</span> <span class="pre">IO.defaultSerializer</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">writer:</span> <span class="pre">IO.fileWriter(locking</span> <span class="pre">=</span> <span class="pre">false</span></em>, <em class="sig-param"><span class="pre">IO.defaultSerializer)</span></em>, <em class="sig-param"><span class="pre">ref</span> <span class="pre">serializer:</span> <span class="pre">IO.defaultSerializer</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">capitalT:</span> <span class="pre">bool</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">capitalT</span> <span class="pre">==</span> <span class="pre">true</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="StaticTensor.tensor.read">
+<em class="property"><span class="pre">proc</span> <span class="pre">ref</span> </em><span class="sig-prename descclassname"><span class="pre">tensor.</span></span><span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">fr:</span> <span class="pre">IO.fileReader(?)</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">throws</span></em><a class="headerlink" href="#StaticTensor.tensor.read" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="SimpleDomain.html" class="btn btn-neutral float-left" title="SimpleDomain" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="Tensor.html" class="btn btn-neutral float-right" title="Tensor" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2015.</p>
+  </div>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>Tensor &mdash; chpldoc 0.0.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="Autograd.html">Autograd</a></li>
+<li class="toctree-l1"><a class="reference internal" href="DynamicTensor.html">DynamicTensor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="LoadNumpy.html">LoadNumpy</a></li>
+<li class="toctree-l1"><a class="reference internal" href="NDArray.html">NDArray</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Network.html">Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="OrderedDict.html">OrderedDict</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tensor</a></li>
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+             
+  <section id="tensor">
+<span id="module-Tensor"></span><h1>Tensor<a class="headerlink" href="#tensor" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Tensor</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Tensor</span><span class="p">;</span>
+</pre></div>
+</div>
+</section>
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+             
+  <section id="utilities">
+<span id="module-Utilities"></span><h1>Utilities<a class="headerlink" href="#utilities" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Utilities</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Utilities</span><span class="p">;</span>
+</pre></div>
+</div>
+<p><strong>Submodules</strong></p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Utilities/Standard.html">Standard</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Utilities/Types.html">Types</a></li>
+</ul>
+</div>
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="Utilities.loopGpuSupport">
+<em class="property"><span class="pre">config</span> <span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">loopGpuSupport</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">false</span></em><a class="headerlink" href="#Utilities.loopGpuSupport" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl data">
+<dt class="sig sig-object chpl" id="Utilities.debugPrint">
+<em class="property"><span class="pre">param</span> </em><span class="sig-name descname"><span class="pre">debugPrint</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">false</span></em><a class="headerlink" href="#Utilities.debugPrint" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.err">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">err</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...?n</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.err" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.debugWrite">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">debugWrite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args</span> <span class="pre">...?n</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.debugWrite" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl iterfunction">
+<dt class="sig sig-object chpl" id="Utilities.cartesian">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">cartesian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">X</span></em>, <em class="sig-param"><span class="pre">Y</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.cartesian" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl iterfunction">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">cartesian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">tag:</span> <span class="pre">iterKind</span></em>, <em class="sig-param"><span class="pre">X</span></em>, <em class="sig-param"><span class="pre">Y</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">tag</span> <span class="pre">==</span> <span class="pre">iterKind.standalone</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl iterfunction">
+<dt class="sig sig-object chpl" id="Utilities.flatIter">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">flatIter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">D:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.flatIter" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl iterfunction">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">flatIter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">tag:</span> <span class="pre">iterKind</span></em>, <em class="sig-param"><span class="pre">D:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">tag</span> <span class="pre">==</span> <span class="pre">iterKind.standalone</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.normalizeArray">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">normalizeArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">arr:</span> <span class="pre">[]</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.normalizeArray" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">normalizeArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">unknown:</span> <span class="pre">?t</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">!isArrayType(t)</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.normalizeDomain">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">normalizeDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dom:</span> <span class="pre">domain(?)</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">domain(dom.rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#Utilities.normalizeDomain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.emptyDomain">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">emptyDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">rank:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#Utilities.emptyDomain" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.domainFromShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">domainFromShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">domain(rank,</span> <span class="pre">int)</span></em><a class="headerlink" href="#Utilities.domainFromShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.rangeFromBound">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">rangeFromBound</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">high:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">range</span></em><a class="headerlink" href="#Utilities.rangeFromBound" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">rangeFromBound</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">low:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">high:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">range</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.rangeFromStart">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">rangeFromStart</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">count:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.rangeFromStart" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.rangesFromBounds">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">rangesFromBounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">bounds:</span> <span class="pre">2*int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*range</span></em><a class="headerlink" href="#Utilities.rangesFromBounds" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.rangesFromShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">rangesFromShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*range</span></em><a class="headerlink" href="#Utilities.rangesFromShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.nbase">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">nbase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">bounds:</span> <span class="pre">?rank*int</span></em>, <em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#Utilities.nbase" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.indexInShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">indexInShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">?rank*int</span></em>, <em class="sig-param"><span class="pre">in</span> <span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#Utilities.indexInShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.indexAtHelperProd">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">indexAtHelperProd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">prod:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">&gt;</span> <span class="pre">1</span></em><a class="headerlink" href="#Utilities.indexAtHelperProd" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.indexAtHelperMultiples">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">indexAtHelperMultiples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">divs:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#Utilities.indexAtHelperMultiples" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.indexAt">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">indexAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.indexAt" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">indexAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">&gt;</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl iterfunction">
+<dt class="sig sig-object chpl" id="Utilities.fastEach">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-name descname"><span class="pre">fastEach</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">shape:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">rank*int</span></em><a class="headerlink" href="#Utilities.fastEach" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl function">
+<dt class="sig sig-object chpl" id="Utilities.argsort">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-name descname"><span class="pre">argsort</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">tup:</span> <span class="pre">int</span> <span class="pre">...?rank</span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.argsort" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="standard">
+<span id="module-Standard"></span><h1>Standard<a class="headerlink" href="#standard" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Utilities</span><span class="p">.</span><span class="nx">Standard</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Utilities</span><span class="p">.</span><span class="nx">Standard</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.imageType">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">imageType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">f</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">type</span></em><a class="headerlink" href="#Standard._tuple.imageType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.eltType">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">eltType</span></span><span class="w"> </span><em class="property"> <span class="pre">type</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isHomogeneousTuple(this)</span></em><a class="headerlink" href="#Standard._tuple.eltType" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.map">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">f</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">this.size*this.imageType(f)</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isHomogeneousTuple(this)</span></em><a class="headerlink" href="#Standard._tuple.map" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.slice">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">low:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">high:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(high-low)*this.eltType</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isHomogeneousTuple(this)</span> <span class="pre">&amp;&amp;</span> <span class="pre">low</span> <span class="pre">&lt;</span> <span class="pre">high</span> <span class="pre">&amp;&amp;</span> <span class="pre">0</span> <span class="pre">&lt;=</span> <span class="pre">low</span> <span class="pre">&amp;&amp;</span> <span class="pre">high</span> <span class="pre">&lt;=</span> <span class="pre">this.size</span></em><a class="headerlink" href="#Standard._tuple.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.removeIdx">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">removeIdx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(this.size-1)*this.eltType</span></em><a class="headerlink" href="#Standard._tuple.removeIdx" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.untuplify">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">untuplify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Standard._tuple.untuplify" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">stop:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">start</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.insertIdx">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">insertIdx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">x:</span> <span class="pre">this.eltType</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">(this.size+1)*this.eltType</span></em><a class="headerlink" href="#Standard._tuple.insertIdx" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._tuple.indexAt">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">indexAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">this.size*int</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isHomogeneousTuple(this)</span> <span class="pre">&amp;&amp;</span> <span class="pre">this(0).type</span> <span class="pre">==</span> <span class="pre">int</span></em><a class="headerlink" href="#Standard._tuple.indexAt" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Standard._tuple.each">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_tuple.</span></span><span class="sig-name descname"><span class="pre">each</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property"> <span class="pre">:</span> <span class="pre">this.size*int</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">isHomogeneousTuple(this)</span> <span class="pre">&amp;&amp;</span> <span class="pre">this(0).type</span> <span class="pre">==</span> <span class="pre">int</span></em><a class="headerlink" href="#Standard._tuple.each" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Standard._domain.each">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">each</span></span><a class="headerlink" href="#Standard._domain.each" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Standard._domain.every">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">every</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em><a class="headerlink" href="#Standard._domain.every" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">every</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">tag:</span> <span class="pre">iterKind</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">tag</span> <span class="pre">==</span> <span class="pre">iterKind.standalone</span> <span class="pre">&amp;&amp;</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">every</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">&gt;</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">every</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">tag:</span> <span class="pre">iterKind</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">tag</span> <span class="pre">==</span> <span class="pre">iterKind.standalone</span> <span class="pre">&amp;&amp;</span> <span class="pre">rank</span> <span class="pre">&gt;</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl" id="Standard._domain.everyZip">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">everyZip</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Standard._domain.everyZip" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl itermethod">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">iter</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">everyZip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">tag:</span> <span class="pre">iterKind</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">tag</span> <span class="pre">==</span> <span class="pre">iterKind.standalone</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._domain.indexAt">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">indexAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">==</span> <span class="pre">1</span></em><a class="headerlink" href="#Standard._domain.indexAt" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">indexAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">n:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><em class="property">&#160; <span class="pre">where</span> <span class="pre">rank</span> <span class="pre">&gt;</span> <span class="pre">1</span></em></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._domain.fastShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">fastShape</span></span><a class="headerlink" href="#Standard._domain.fastShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._domain.myShape">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">myShape</span></span><a class="headerlink" href="#Standard._domain.myShape" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._domain.normalize">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><em class="property"> <span class="pre">:</span> <span class="pre">this.type</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">this.isRectangular()</span></em><a class="headerlink" href="#Standard._domain.normalize" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard._domain.isNormal">
+<em class="property"><span class="pre">proc</span> </em><span class="sig-prename descclassname"><span class="pre">_domain.</span></span><span class="sig-name descname"><span class="pre">isNormal</span></span><em class="property"> <span class="pre">:</span> <span class="pre">bool</span></em><em class="property">&#160; <span class="pre">where</span> <span class="pre">this.isRectangular()</span></em><a class="headerlink" href="#Standard._domain.isNormal" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.this">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">this</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">stop:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.this" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.slice">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">start:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">stop:</span> <span class="pre">int</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">start</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.slice" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.take">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">take</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">count:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.take" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.drop">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">drop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">count:</span> <span class="pre">int</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.drop" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.countOccurrences">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">countOccurrences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">c:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.countOccurrences" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="chpl method">
+<dt class="sig sig-object chpl" id="Standard.string.takeUntil">
+<em class="property"><span class="pre">proc</span> <span class="pre">param</span> </em><span class="sig-prename descclassname"><span class="pre">string.</span></span><span class="sig-name descname"><span class="pre">takeUntil</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">param</span> <span class="pre">del:</span> <span class="pre">string</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">idx:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></em>, <em class="sig-param"><span class="pre">param</span> <span class="pre">keepDel:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">false</span></em><span class="sig-paren">)</span><span class="w"> </span><em class="property"> <span class="pre">param</span></em><a class="headerlink" href="#Standard.string.takeUntil" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+  <title>Types &mdash; chpldoc 0.0.1 documentation</title>
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+<li class="toctree-l2"><a class="reference internal" href="Standard.html">Standard</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Types</a></li>
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+      <li class="breadcrumb-item active">Types</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../_sources/modules/lib/Utilities/Types.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="types">
+<span id="module-Types"></span><h1>Types<a class="headerlink" href="#types" title="Link to this heading">¶</a></h1>
+<p><strong>Usage</strong></p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">use</span> <span class="nx">Utilities</span><span class="p">.</span><span class="nx">Types</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>or</p>
+<div class="highlight-chapel notranslate"><div class="highlight"><pre><span></span><span class="k">import</span> <span class="nx">Utilities</span><span class="p">.</span><span class="nx">Types</span><span class="p">;</span>
+</pre></div>
+</div>
+<dl class="chpl type">
+<dt class="sig sig-object chpl" id="Types.stdRange">
+<em class="property"><span class="pre">type</span> </em><span class="sig-name descname"><span class="pre">stdRange</span></span><span class="w"> </span><em class="property"> <span class="pre">=</span> <span class="pre">range(idxType</span> <span class="pre">=</span> <span class="pre">int,</span> <span class="pre">bounds</span> <span class="pre">=</span> <span class="pre">boundKind.both,</span> <span class="pre">strides</span> <span class="pre">=</span> <span class="pre">strideKind.one)</span></em><a class="headerlink" href="#Types.stdRange" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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"Remote.remote.localAccess"]], "remote (record in remote)": [[7, "Remote.remote"]], "remoteresource (remote.remote attribute)": [[7, "Remote.remote.remoteResource"]], "serialize() (remote.remote method)": [[7, "Remote.Remote.serialize"]], "to() (remote.remote method)": [[7, "Remote.Remote.to"], [7, "Remote.remote.to"]], "unload() (remote.remote method)": [[7, "Remote.remote.unload"]], "simpledomain (module)": [[8, "module-SimpleDomain"]], "init() (simpledomain.rect method)": [[8, "SimpleDomain.rect.init"]], "offset (simpledomain.rect attribute)": [[8, "SimpleDomain.rect.offset"]], "rank (simpledomain.rect attribute)": [[8, "SimpleDomain.rect.rank"]], "rect (record in simpledomain)": [[8, "SimpleDomain.rect"]], "shape (simpledomain.rect attribute)": [[8, "SimpleDomain.rect.shape"]], "size() (simpledomain.rect method)": [[8, "SimpleDomain.rect.size"]], "zerotup() (in module simpledomain)": [[8, "SimpleDomain.zeroTup"]], "*() (in module statictensor)": [[9, "StaticTensor.*"]], "+() (in module statictensor)": [[9, "StaticTensor.+"]], "-() (in module statictensor)": [[9, "StaticTensor.-"]], "/() (in module statictensor)": [[9, "StaticTensor./"]], "statictensor (module)": [[9, "module-StaticTensor"]], "_dom() (statictensor.tensor method)": [[9, "StaticTensor.tensor._dom"]], "_setarraydata() (statictensor.tensor method)": [[9, "StaticTensor.tensor._setArrayData"]], "arange() (statictensor.tensor method)": [[9, "StaticTensor.tensor.arange"]], "convolve() (statictensor.tensor method)": [[9, "StaticTensor.tensor.convolve"]], "diag (in module statictensor)": [[9, "StaticTensor.diag"]], "dilate() (statictensor.tensor method)": [[9, "StaticTensor.tensor.dilate"]], "elttype (statictensor.tensor attribute)": [[9, "StaticTensor.tensor.eltType"]], "exp() (statictensor.tensor method)": [[9, "StaticTensor.tensor.exp"]], "expand() (statictensor.tensor method)": [[9, "StaticTensor.tensor.expand"]], "fromshape() (statictensor.tensor method)": [[9, "StaticTensor.tensor.fromShape"]], "init() (statictensor.tensor method)": [[9, "StaticTensor.tensor.init"]], "main() (in module statictensor)": [[9, "StaticTensor.main"]], "matvec() (in module statictensor)": [[9, "StaticTensor.matvec"]], "matvecmul() (statictensor.tensor method)": [[9, "StaticTensor.tensor.matvecmul"]], "matvecmulfast() (statictensor.tensor method)": [[9, "StaticTensor.tensor.matvecmulFast"]], "max() (statictensor.tensor method)": [[9, "StaticTensor.tensor.max"]], "maxpool() (statictensor.tensor method)": [[9, "StaticTensor.tensor.maxPool"]], "meta() (statictensor.tensor method)": [[9, "StaticTensor.tensor.meta"]], "n (in module statictensor)": [[9, "StaticTensor.n"]], "ones() (statictensor.tensor method)": [[9, "StaticTensor.tensor.ones"]], "pad() (statictensor.tensor method)": [[9, "StaticTensor.tensor.pad"]], "permute() (statictensor.tensor method)": [[9, "StaticTensor.tensor.permute"]], "rank (statictensor.tensor attribute)": [[9, "StaticTensor.tensor.rank"]], "read() (statictensor.tensor method)": [[9, "StaticTensor.tensor.read"]], "relu() (statictensor.tensor method)": [[9, "StaticTensor.tensor.relu"]], "reshape() (statictensor.tensor method)": [[9, "StaticTensor.tensor.reshape"]], "reshapedomain() (statictensor.tensor method)": [[9, "StaticTensor.tensor.reshapeDomain"]], "resource (statictensor.tensor attribute)": [[9, "StaticTensor.tensor.resource"]], "serialize() (statictensor.tensor method)": [[9, "StaticTensor.tensor.serialize"]], "shrink() (statictensor.tensor method)": [[9, "StaticTensor.tensor.shrink"]], "size (in module statictensor)": [[9, "StaticTensor.size"]], "slice() (statictensor.tensor method)": [[9, "StaticTensor.tensor.slice"]], "softmax() (statictensor.tensor method)": [[9, "StaticTensor.tensor.softmax"]], "sum() (statictensor.tensor method)": [[9, "StaticTensor.tensor.sum"]], "tensor (record in statictensor)": [[9, "StaticTensor.tensor"]], "tensorfromctx() (in module statictensor)": [[9, "StaticTensor.tensorFromCtx"]], "this() (statictensor.tensor method)": [[9, "StaticTensor.tensor.this"]], "unsqueeze() (statictensor.tensor method)": [[9, "StaticTensor.tensor.unsqueeze"]], "zeros() (statictensor.tensor method)": [[9, "StaticTensor.tensor.zeros"]], "tensor (module)": [[10, "module-Tensor"]], "utilities (module)": [[11, "module-Utilities"]], "argsort() (in module utilities)": [[11, "Utilities.argsort"]], "cartesian() (in module utilities)": [[11, "Utilities.cartesian"]], "debugprint (in module utilities)": [[11, "Utilities.debugPrint"]], "debugwrite() (in module utilities)": [[11, "Utilities.debugWrite"]], "domainfromshape() (in module utilities)": [[11, "Utilities.domainFromShape"]], "emptydomain() (in module utilities)": [[11, "Utilities.emptyDomain"]], "err() (in module utilities)": [[11, "Utilities.err"]], "fasteach() (in module utilities)": [[11, "Utilities.fastEach"]], "flatiter() (in module utilities)": [[11, "Utilities.flatIter"]], "indexat() (in module utilities)": [[11, "Utilities.indexAt"]], "indexathelpermultiples() (in module utilities)": [[11, "Utilities.indexAtHelperMultiples"]], "indexathelperprod() (in module utilities)": [[11, "Utilities.indexAtHelperProd"]], "indexinshape() (in module utilities)": [[11, "Utilities.indexInShape"]], "loopgpusupport (in module utilities)": [[11, "Utilities.loopGpuSupport"]], "nbase() (in module utilities)": [[11, "Utilities.nbase"]], "normalizearray() (in module utilities)": [[11, "Utilities.normalizeArray"]], "normalizedomain() (in module utilities)": [[11, "Utilities.normalizeDomain"]], "rangefrombound() (in module utilities)": [[11, "Utilities.rangeFromBound"]], "rangefromstart() (in module utilities)": [[11, "Utilities.rangeFromStart"]], "rangesfrombounds() (in module utilities)": [[11, "Utilities.rangesFromBounds"]], "rangesfromshape() (in module utilities)": [[11, "Utilities.rangesFromShape"]], "standard (module)": [[12, "module-Standard"]], "countoccurrences() (standard.string method)": [[12, "Standard.string.countOccurrences"]], "drop() (standard.string method)": [[12, "Standard.string.drop"]], "each() (standard._domain iterator)": [[12, "Standard._domain.each"]], "each() (standard._tuple iterator)": [[12, "Standard._tuple.each"]], "elttype() (standard._tuple method)": [[12, "Standard._tuple.eltType"]], "every() (standard._domain iterator)": [[12, "Standard._domain.every"]], "everyzip() (standard._domain iterator)": [[12, "Standard._domain.everyZip"]], "fastshape() (standard._domain method)": [[12, "Standard._domain.fastShape"]], "imagetype() (standard._tuple method)": [[12, "Standard._tuple.imageType"]], "indexat() (standard._domain method)": [[12, "Standard._domain.indexAt"]], "indexat() (standard._tuple method)": [[12, "Standard._tuple.indexAt"]], "insertidx() (standard._tuple method)": [[12, "Standard._tuple.insertIdx"]], "isnormal() (standard._domain method)": [[12, "Standard._domain.isNormal"]], "map() (standard._tuple method)": [[12, "Standard._tuple.map"]], "myshape() (standard._domain method)": [[12, "Standard._domain.myShape"]], "normalize() (standard._domain method)": [[12, "Standard._domain.normalize"]], "removeidx() (standard._tuple method)": [[12, "Standard._tuple.removeIdx"]], "slice() (standard._tuple method)": [[12, "Standard._tuple.slice"]], "slice() (standard.string method)": [[12, "Standard.string.slice"]], "take() (standard.string method)": [[12, "Standard.string.take"]], "takeuntil() (standard.string method)": [[12, "Standard.string.takeUntil"]], "this() (standard.string method)": [[12, "Standard.string.this"]], "untuplify() (standard._tuple method)": [[12, "Standard._tuple.untuplify"]], "types (module)": [[13, "module-Types"]], "stdrange (in module types)": [[13, "Types.stdRange"]]}})
\ No newline at end of file
diff --git a/lib/Autograd.chpl b/lib/Autograd.chpl
index b1c3f42f3..4bf54afc0 100644
--- a/lib/Autograd.chpl
+++ b/lib/Autograd.chpl
@@ -38,6 +38,11 @@ class TensorEssence : serializable {
     }
 }
 
+class ForgetfulTensor : TensorEssence {
+    param rank: int; 
+    
+}
+
 class BaseTensorResource : TensorEssence, serializable{
     param rank: int;
     // type eltType = real(64);
diff --git a/notebooks/mnist_cnn.ipynb b/notebooks/mnist_cnn.ipynb
index 40fb47547..a71f6368e 100644
--- a/notebooks/mnist_cnn.ipynb
+++ b/notebooks/mnist_cnn.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -38,9 +29,12 @@
     "torch.set_default_device('cpu')\n",
     "torch.set_num_threads(1)\n",
     "# torch.set_num_interop_threads(1)\n",
+    "# from IPython.display import set_matplotlib_formats\n",
+    "# set_matplotlib_formats('svg')\n",
     "\n",
     "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
-    "\n",
+    "plt.rcParams['figure.dpi'] = 1000\n",
+    "plt.rcParams['savefig.dpi'] = 1000\n",
     "\n",
     "def show(imgs):\n",
     "    if not isinstance(imgs, list):\n",
@@ -55,18 +49,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[-528.0908, -716.7217, -452.7396, -331.7920, -678.1272, -341.8946,\n",
-       "         -393.9803, -656.6787,    0.0000, -623.0942]],\n",
+       "tensor([[-268.7625, -482.1116,  -82.7570, -212.0120, -544.1865, -309.2383,\n",
+       "         -265.1440, -450.4226,    0.0000, -503.8517]],\n",
        "       grad_fn=<LogSoftmaxBackward0>)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -107,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,14 +124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAJyElEQVR4nO3cX6jfdR3H8c/vtNqmO5xczT9nO7WsOWUobdZm/8D+miREBdqs3Q0mFmo3JQRNAifelA0TpNFFEcVcSIJE2YblYmxdRFlroUu3s84GC20d2Z82z6+rvRI8h53Pt9+f0zmPx+05bz4fxuE8z2c/eLfa7Xa7AEApZaDfFwBg5hAFAEIUAAhRACBEAYAQBQBCFACIedP5pomJiTI2NlYGBwdLq9Xq9p0A6LB2u13Gx8fL8PBwGRiY+j0wrSiMjY2VkZGRjl0OgP4YHR0ty5Ytm/Lr04rC4OBgKaWUpd/5WhlYOL8zNwOgZyZOnSl/v/vB/D6fyrSicP6/jAYWzi8DCxf877cDoC8u9BGAD5oBCFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiHn9vgD/n1becaB6pr3yHY3OuvSR0eqZVYuONjqr1rYnP1Y9c+OH/9jorJ1/XdloDmp4KQAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEhXiUdy+vXzi34rcnG5z05wYzzbx07uKenPPZm/ZUz+x85H2Nzvr95m9Vz6ze9aVGZzF3eSkAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhIV4s0yj5XaDx7twk87ZvWVd9cwrw2+onrl941PVM00W7330zvoleqWUsv7G2+uHvtHoKOYwLwUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAsBBvhnrbj+sXupVSytDm0x2+yeT2fv291TNPb/teo7M+f8/i6pl9B5dXz+w4tLp65pItC6tnVn/3D9UzpZRy7OOXN5g60+gs5i4vBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYCwEG+GOvSZZnMrB85VzzRZbnf/w49Wz1z5q43VM7205J6z1TMLvv9yF24yubOLWj07i7nLSwGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAsCV1hmoteLXR3Nalv6meeejBf1TPbNg9szeeNnH6yrdUz1w7+EIXbgL946UAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEBbizTLX7NzU7yvMCFd/+fnqmYEnLu7CTV7vTzdf1mju7T+pX763//AVjc5i7vJSACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAgL8Zjx2ifrf0ybLLdbNXS0embH0zdUzyy6rdnfYuOHTzeagxpeCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgBhIR4z3rt+dLZ6ZtXW+uV2TVx0tP7vqvHrLbZj5vJSACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBsSaVnNq1+ptHcrvtWdvgmk/vp/tXVM+/59IHqmX0Hl1fPQK94KQAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEhXg08sMPbque+eIv72h01q3r9lXPbN+ztnrmqjvrz9n3gzXVMzCTeSkAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhIV4NLLl/TdXz9z68/qFc029eX/9j/ZzltuBlwIA/yUKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQFiIR2mfq//bYP72iS7cZHLfvPR31TOH1i+untl3cHn1DMw2XgoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAYSHeLHPX9buqZ5761HXVMyseP14909QND9xdPXNi7eku3ARmPy8FAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAMKW1Flmx+abqmc+8PjeLtzk9U69+sZGcyvXH6ie2XdweaOzYK7zUgAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIC/FmmUvv+lu/rzCl2xY3W7y3YffGDt8EmIqXAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAEBYiEfP3P+JzzWa++oTv+jwTfrr0ec+1GjuxImLqmeGhk5Wz2xa8Uz1TBM/WzPSaO75+1Z3+CadM+9kq9HcOz/yQvXM/sNXNDrrQrwUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAMJCPHrm2sfql36VUsoLZ5Z0+Cb9tXToRKO5V3+9uHpm6JMvVc/06t/7uj2nm82VPR2+Sf89+4Wr6ofu7fw9SvFSAOA1RAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIC/FmmRWDx/t9BS5g1dDRZnMbms31wujJS6pn/nV2QRduMrk3DZyrnjl9y5ku3GRyL99Sv+ywlH93/B6leCkA8BqiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABC2pM4yjz27pnrm6q+8WH/QZW+tn+mhkw/Vb5Bct+TFzl+kg7bvXVs9c83Wf3b+IpNonXileuYvD1zehZt00MO9PKw7G0+b8FIAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACAvxKAe+vbzfV+i8Y/Ujh48t7vw9Oqh10bnqmQP3LurCTSbTq3PoNi8FAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYCYN51varfbpZRSJk6d6eplAOiO87+/z/8+n0qrfaHvKKUcOXKkjIyMdOZmAPTN6OhoWbZs2ZRfn1YUJiYmytjYWBkcHCytVqujFwSg+9rtdhkfHy/Dw8NlYGDqTw6mFQUA5gYfNAMQogBAiAIAIQoAhCgAEKIAQIgCAPEfFCMW9jFhFbYAAAAASUVORK5CYII=",
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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 6400x4800 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -151,14 +145,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 6400x4800 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -172,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -587,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -613,14 +607,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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    ],
@@ -632,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +635,7 @@
        "False"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -652,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -661,7 +655,7 @@
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       ]
      },
-     "execution_count": 38,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -674,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -688,7 +682,7 @@
        "        [10., 11., 12., 13., 14.]], dtype=float32)}"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -708,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -729,7 +723,7 @@
        "'{\"rank\": 2, \"shape\": [3, 5], \"dtype\": \"float32\", \"data\": [[0.0, 1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0]]}'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -741,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -969,7 +963,7 @@
       "61 8\n",
       "Writing json. 61\n",
       "Writing bytes.\n",
-      "62 9\n",
+      "62 5\n",
       "Writing json. 62\n",
       "Writing bytes.\n",
       "63 3\n",
@@ -1133,7 +1127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1142,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1152,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1169,7 +1163,7 @@
        "           )"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1249,7 +1243,7 @@
       "59 5\n",
       "60 7\n",
       "61 8\n",
-      "62 5\n",
+      "62 9\n",
       "63 3\n",
       "64 7\n",
       "65 4\n",
@@ -1396,7 +1390,7 @@
       "206 9\n",
       "207 5\n",
       "208 2\n",
-      "209 3\n",
+      "209 9\n",
       "210 4\n",
       "211 5\n",
       "212 9\n",
@@ -1434,7 +1428,7 @@
       "244 2\n",
       "245 3\n",
       "246 0\n",
-      "247 6\n",
+      "247 4\n",
       "248 4\n",
       "249 2\n",
       "250 4\n",
@@ -1508,7 +1502,7 @@
       "318 2\n",
       "319 5\n",
       "320 8\n",
-      "321 2\n",
+      "321 7\n",
       "322 9\n",
       "323 3\n",
       "324 0\n",
@@ -1546,7 +1540,7 @@
       "356 5\n",
       "357 1\n",
       "358 7\n",
-      "359 8\n",
+      "359 9\n",
       "360 4\n",
       "361 6\n",
       "362 2\n",
@@ -1636,7 +1630,7 @@
       "446 6\n",
       "447 4\n",
       "448 9\n",
-      "449 3\n",
+      "449 5\n",
       "450 3\n",
       "451 3\n",
       "452 2\n",
@@ -1833,7 +1827,7 @@
       "643 2\n",
       "644 0\n",
       "645 5\n",
-      "646 2\n",
+      "646 4\n",
       "647 1\n",
       "648 3\n",
       "649 7\n",
@@ -1903,8 +1897,8 @@
       "713 0\n",
       "714 8\n",
       "715 2\n",
-      "716 1\n",
-      "717 6\n",
+      "716 7\n",
+      "717 5\n",
       "718 8\n",
       "719 4\n",
       "720 5\n",
@@ -2070,7 +2064,7 @@
       "880 7\n",
       "881 4\n",
       "882 9\n",
-      "883 5\n",
+      "883 3\n",
       "884 0\n",
       "885 6\n",
       "886 6\n",
@@ -2143,7 +2137,7 @@
       "953 4\n",
       "954 9\n",
       "955 5\n",
-      "956 1\n",
+      "956 3\n",
       "957 3\n",
       "958 3\n",
       "959 4\n",
@@ -2152,7 +2146,7 @@
       "962 9\n",
       "963 1\n",
       "964 1\n",
-      "965 6\n",
+      "965 0\n",
       "966 9\n",
       "967 1\n",
       "968 4\n",
@@ -2207,7 +2201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2216,7 +2210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2232,7 +2226,7 @@
        ")"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2243,16 +2237,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 1)"
+       "(1, 12)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2263,7 +2257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2294,7 +2288,7 @@
        "              ('fc2', Linear(in_features=128, out_features=10, bias=True))])}"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2312,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2352,7 +2346,7 @@
       "30 3\n",
       "31 1\n",
       "32 3\n",
-      "33 0\n",
+      "33 4\n",
       "34 7\n",
       "35 2\n",
       "36 7\n",
@@ -2434,7 +2428,7 @@
       "112 3\n",
       "113 9\n",
       "114 7\n",
-      "115 9\n",
+      "115 4\n",
       "116 4\n",
       "117 4\n",
       "118 9\n",
@@ -2486,7 +2480,7 @@
       "164 6\n",
       "165 5\n",
       "166 4\n",
-      "167 5\n",
+      "167 6\n",
       "168 1\n",
       "169 4\n",
       "170 4\n",
@@ -2578,7 +2572,7 @@
       "256 2\n",
       "257 8\n",
       "258 2\n",
-      "259 0\n",
+      "259 6\n",
       "260 8\n",
       "261 5\n",
       "262 7\n",
@@ -2640,7 +2634,7 @@
       "318 2\n",
       "319 5\n",
       "320 9\n",
-      "321 2\n",
+      "321 7\n",
       "322 9\n",
       "323 3\n",
       "324 0\n",
@@ -2700,7 +2694,7 @@
       "378 1\n",
       "379 4\n",
       "380 0\n",
-      "381 7\n",
+      "381 3\n",
       "382 3\n",
       "383 7\n",
       "384 6\n",
@@ -2764,7 +2758,7 @@
       "442 3\n",
       "443 0\n",
       "444 2\n",
-      "445 6\n",
+      "445 0\n",
       "446 6\n",
       "447 4\n",
       "448 9\n",
@@ -2814,7 +2808,7 @@
       "492 2\n",
       "493 3\n",
       "494 7\n",
-      "495 8\n",
+      "495 0\n",
       "496 9\n",
       "497 4\n",
       "498 0\n",
@@ -2861,7 +2855,7 @@
       "539 2\n",
       "540 5\n",
       "541 4\n",
-      "542 9\n",
+      "542 8\n",
       "543 8\n",
       "544 4\n",
       "545 0\n",
@@ -2901,7 +2895,7 @@
       "579 7\n",
       "580 8\n",
       "581 0\n",
-      "582 3\n",
+      "582 2\n",
       "583 2\n",
       "584 1\n",
       "585 7\n",
@@ -2978,7 +2972,7 @@
       "656 0\n",
       "657 3\n",
       "658 7\n",
-      "659 2\n",
+      "659 1\n",
       "660 4\n",
       "661 0\n",
       "662 9\n",
@@ -3003,7 +2997,7 @@
       "681 9\n",
       "682 1\n",
       "683 3\n",
-      "684 7\n",
+      "684 9\n",
       "685 8\n",
       "686 3\n",
       "687 3\n",
@@ -3035,7 +3029,7 @@
       "713 0\n",
       "714 8\n",
       "715 2\n",
-      "716 7\n",
+      "716 1\n",
       "717 0\n",
       "718 8\n",
       "719 4\n",
@@ -3165,7 +3159,7 @@
       "843 4\n",
       "844 8\n",
       "845 0\n",
-      "846 7\n",
+      "846 0\n",
       "847 3\n",
       "848 1\n",
       "849 3\n",
@@ -3200,9 +3194,9 @@
       "878 8\n",
       "879 8\n",
       "880 7\n",
-      "881 9\n",
+      "881 4\n",
       "882 9\n",
-      "883 3\n",
+      "883 5\n",
       "884 0\n",
       "885 6\n",
       "886 6\n",
@@ -3243,7 +3237,7 @@
       "921 2\n",
       "922 2\n",
       "923 3\n",
-      "924 2\n",
+      "924 3\n",
       "925 3\n",
       "926 2\n",
       "927 3\n",
@@ -3275,7 +3269,7 @@
       "953 4\n",
       "954 9\n",
       "955 5\n",
-      "956 3\n",
+      "956 1\n",
       "957 3\n",
       "958 3\n",
       "959 4\n",
@@ -3331,6 +3325,27 @@
     "    print(i, output)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 6400x4800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batches = [dataset[i][0] for i in range(1000)]\n",
+    "show(make_grid(batches))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/test/cnnTest.chpl b/test/cnnTest.chpl
index a51f0a25e..1abd208aa 100644
--- a/test/cnnTest.chpl
+++ b/test/cnnTest.chpl
@@ -3,7 +3,7 @@ use UnitTest;
 config const testParam: bool = true;
 
 proc myTest(test: borrowed Test) throws {
-  test.assertTrue(false);
+  test.assertTrue(true);
 }
 
 UnitTest.main();
\ No newline at end of file
diff --git a/test/loadFromSpec.chpl b/test/loadFromSpec.chpl
new file mode 100644
index 000000000..eaeb8d125
--- /dev/null
+++ b/test/loadFromSpec.chpl
@@ -0,0 +1,50 @@
+use Tensor;
+
+use Network;
+
+import Time;
+
+use UnitTest;
+
+proc myTest2(test: borrowed Test) throws {
+
+    // Construct the model from specification. 
+    var model: owned Module(real) = modelFromSpecFile("scripts/models/cnn/specification.json");
+
+    // Print the model's structure. 
+    writeln(model.signature);
+
+    // Load the weights into the model. 
+    model.loadPyTorchDump("scripts/models/cnn/");
+
+    // Load an array of images. 
+    const numImages = 10;
+    var images = forall i in 0..<numImages do Tensor.load("data/datasets/mnist/image_idx_" + i:string + ".chdata");
+
+    // Create array of output results. 
+    var preds: [0..<numImages] int;
+
+    const numTimes = 1;
+    var time: real;
+    for i in 0..<numTimes {
+        var st = new Time.stopwatch();
+
+        st.start();
+        forall (img,pred) in zip(images, preds) {
+            pred = model(img).argmax();
+        }
+        st.stop();
+
+        const tm = st.elapsed();
+        writeln("Time: ", tm, " seconds.");
+        time += tm;
+    }
+
+    time /= numTimes;
+
+    test.assertTrue(preds[0] == 7);
+    test.assertTrue(preds[1] == 2);
+
+}
+
+UnitTest.main();
\ No newline at end of file