Can't infer Python side-effects for tf.keras.Models with implicit callables

[khatchad]

Consider the following model:

Hybridize-Functions-Refactoring/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel2/in/A.py

Lines 6 to 30 in 6b7717c

	class SequentialModel(tf.keras.Model):
	def __init__(self, **kwargs):
	super(SequentialModel, self).__init__(**kwargs)
	self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
	# Add a lot of small layers
	num_layers = 100
	self.my_layers = [tf.keras.layers.Dense(64, activation="relu")
	for n in range(num_layers)]
	self.dropout = tf.keras.layers.Dropout(0.2)
	self.dense_2 = tf.keras.layers.Dense(10)
	def call(self, x):
	x = self.flatten(x)
	for layer in self.my_layers:
	x = layer(x)
	x = self.dropout(x)
	x = self.dense_2(x)
	return x

And the following client code:

Hybridize-Functions-Refactoring/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel2/in/A.py

Lines 39 to 40 in 6b7717c

	model = SequentialModel()
	result = model(input_data)

This code implicitly calls SequentialModel.call() because Model's __call__() method calls Model.call() and SequentialModel overrides call().

[khatchad]

Blocked on wala/ML#106.