Cleaned up SimpleDomain quite a bit.

docs/_sources/modules/examples/ConvLayerTest.rst.txt

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+.. default-domain:: chpl
+
+.. module:: ConvLayerTest
+
+ConvLayerTest
+=============
+**Usage**
+
+.. code-block:: chapel
+
+   use ConvLayerTest;
+
+
+or
+
+.. code-block:: chapel
+
+   import ConvLayerTest;
+
+.. data:: var t: Tensor(real) = Tensor.arange(1, 8, 8)
+
+.. data:: var conv = new Conv2D(1, 1, 3, 1)
+
+.. data:: var output = conv(t)
+

docs/_sources/modules/examples/MNISTNet.rst.txt

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+.. default-domain:: chpl
+
+.. module:: MNISTNet
+
+MNISTNet
+========
+**Usage**
+
+.. code-block:: chapel
+
+   use MNISTNet;
+
+
+or
+
+.. code-block:: chapel
+
+   import MNISTNet;
+
+.. data:: config param layerDebug = false
+
+.. type:: type dtype = real(32)
+
+.. class:: CNN : Module(?)
+
+   .. attribute:: var conv1: owned(Conv2D(eltType))
+
+   .. attribute:: var conv2: owned(Conv2D(eltType))
+
+   .. attribute:: var dropout1: owned(Dropout(eltType))
+
+   .. attribute:: var dropout2: owned(Dropout(eltType))
+
+   .. attribute:: var flatten: owned(Flatten(eltType))
+
+   .. attribute:: var fc1: owned(Linear(eltType))
+
+   .. attribute:: var fc2: owned(Linear(eltType))
+
+   .. method:: proc  init(type eltType = dtype) 
+
+   .. method:: override proc  forward( input: Tensor(eltType)) : Tensor(eltType)
+
+.. data:: config const diag = false
+
+.. data:: var cnn = new CNN(dtype)
+
+.. data:: var model = Network.loadModel(specFile = "scripts/models/cnn/specification.json", weightsFolder = "scripts/models/cnn/", dtype = dtype)
+
+.. data:: config const testImgSize = 28
+
+.. data:: var img = Tensor.load("data/datasets/mnist/image_idx_0_7_7.chdata")
+
+.. data:: const modelPath = "data/models/mnist_cnn/"
+
+.. data:: config const imageCount = 0
+
+.. data:: var images = forall i in 0..<imageCount do Tensor.load("data/datasets/mnist/image_idx_" + i : string + ".chdata") : dtype
+
+.. data:: var preds: [images.domain] int
+
+.. data:: config const numTimes = 1
+
+.. data:: var conv1 = new Conv2D(real, channels = 1, features = 32, kernel = 3, stride = 1)
+
+.. data:: var conv2 = new Conv2D(real, channels = 32, features = 64, kernel = 3, stride = 1)
+
+.. data:: config const printResults = false
+
+.. data:: var cnn2 = new Sequential(real, (new Conv2D(real, channels = 1, features = 32, kernel = 3, stride = 1)?, new Conv2D(real, channels = 32, features = 64, kernel = 3, stride = 1)?, new Dropout(real, 0.25)?, new Dropout(real, 0.5)?, new Flatten(real)?, new Linear(real, 9216, 128)?, new Linear(real, 128, 10)?))
+

docs/_sources/modules/examples/ModuleSpec.rst.txt

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+.. default-domain:: chpl
+
+.. module:: ModuleSpec
+
+ModuleSpec
+==========
+**Usage**
+
+.. code-block:: chapel
+
+   use ModuleSpec;
+
+
+or
+
+.. code-block:: chapel
+
+   import ModuleSpec;
+
+.. data:: config const detach = true
+
+.. data:: var model: owned(Module(real)) = modelFromSpecFile("scripts/models/cnn/specification.json")
+
+.. data:: config const numImages = 1
+
+.. data:: var images = forall i in 0..<numImages do Tensor.load("data/datasets/mnist/image_idx_" + i : string + ".chdata")
+
+.. data:: var preds: [0..<numImages] int
+
+.. data:: config const numTimes = 1
+
+.. data:: var time: real
+
+.. data:: config const printResults = false
+

docs/_sources/modules/examples/MultiLocaleInference.rst.txt

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+.. default-domain:: chpl
+
+.. module:: MultiLocaleInference
+
+MultiLocaleInference
+====================
+**Usage**
+
+.. code-block:: chapel
+
+   use MultiLocaleInference;
+
+
+or
+
+.. code-block:: chapel
+
+   import MultiLocaleInference;
+
+.. data:: config const detach = true
+
+.. type:: type dtype = real(32)
+
+.. data:: config const numImages = 1
+
+.. data:: const imagesD = blockDist.createDomain({0..<numImages})
+
+.. data:: var images = forall i in imagesD do Tensor.load("data/datasets/mnist/image_idx_" + i : string + ".chdata") : dtype
+
+.. data:: const localeModelsD = blockDist.createDomain(Locales.domain)
+
+.. data:: var localeModels = forall li in localeModelsD do loadModel(specFile = "scripts/models/cnn/specification.json", weightsFolder = "scripts/models/cnn/", dtype = dtype)
+
+.. data:: var preds: [imagesD] int
+
+.. data:: config const numTries = 1
+
+.. data:: var totalTime: real
+
+.. data:: const averageTime = totalTime / numTries
+
+.. data:: config const printResults = false
+