Remove upper pin on PNL, add lower pin.

Fix stroop example to use input_shapes in place of size.

examples/PsyNeuLink/stroop_conflict_monitoring.py

@@ -5,14 +5,14 @@
 
 # Construct the color naming pathway:
 color_input = pnl.ProcessingMechanism(
-    name="color_input", size=2
+    name="color_input", input_shapes=2
 )  # Note:  default function is Linear
 color_input_to_hidden_wts = np.array([[2, -2], [-2, 2]])
 color_hidden = pnl.ProcessingMechanism(
-    name="color_hidden", size=2, function=pnl.Logistic(bias=-4)
+    name="color_hidden", input_shapes=2, function=pnl.Logistic(bias=-4)
 )
 color_hidden_to_output_wts = np.array([[2, -2], [-2, 2]])
-output = pnl.ProcessingMechanism(name="OUTPUT", size=2, function=pnl.Logistic)
+output = pnl.ProcessingMechanism(name="OUTPUT", input_shapes=2, function=pnl.Logistic)
 color_pathway = [
     color_input,
     color_input_to_hidden_wts,
@@ -22,10 +22,10 @@
 ]
 
 # Construct the word reading pathway (using the same output_layer)
-word_input = pnl.ProcessingMechanism(name="word_input", size=2)
+word_input = pnl.ProcessingMechanism(name="word_input", input_shapes=2)
 word_input_to_hidden_wts = np.array([[3, -3], [-3, 3]])
 word_hidden = pnl.ProcessingMechanism(
-    name="word_hidden", size=2, function=pnl.Logistic(bias=-4)
+    name="word_hidden", input_shapes=2, function=pnl.Logistic(bias=-4)
 )
 word_hidden_to_output_wts = np.array([[3, -3], [-3, 3]])
 word_pathway = [
@@ -37,8 +37,8 @@
 ]
 
 # Construct the task specification pathways
-task_input = pnl.ProcessingMechanism(name="task_input", size=2)
-task = pnl.LCAMechanism(name="TASK", size=2, initial_value=[0.5, 0.5])
+task_input = pnl.ProcessingMechanism(name="task_input", input_shapes=2)
+task = pnl.LCAMechanism(name="TASK", input_shapes=2, initial_value=[0.5, 0.5])
 task_color_wts = np.array([[4, 4], [0, 0]])
 task_word_wts = np.array([[0, 0], [4, 4]])
 task_color_pathway = [task_input, task, task_color_wts, color_hidden]
@@ -53,7 +53,7 @@
     name="CONTROL",
     objective_mechanism=pnl.ObjectiveMechanism(
         name="Conflict Monitor",
-        function=pnl.Energy(size=2, matrix=[[0, -2.5], [-2.5, 0]]),
+        function=pnl.Energy(input_shapes=2, matrix=[[0, -2.5], [-2.5, 0]]),
         monitor=output,
     ),
     default_allocation=[0.5],

setup.cfg

@@ -55,7 +55,7 @@ package_dir =
 
 [options.extras_require]
 psyneulink =
-    psyneulink<=0.15.1.0
+    psyneulink>=0.15.3
 
 neuroml =
     pyNeuroML>=1.0.10