message: [docs]: Be consistent about overriding model.call() (vs. model.predict()).
  Fixes
diff: @@ -421,7 +421,7 @@ class Model(tf.keras.Model):
  super(Model, self).__init__()
  self.W = tfe.Variable(5., name='weight')
  self.B = tfe.Variable(10., name='bias')
  - def predict(self, inputs):
  + def call(self, inputs):
  return inputs * self.W + self.B
  # A toy dataset of points around 3 * x + 2
  @@ -432,7 +432,7 @@ training_outputs = training_inputs * 3 + 2 + noise
  # The loss function to be optimized
  def loss(model, inputs, targets):
  - error = model.predict(inputs) - targets
  + error = model(inputs) - targets
  return tf.reduce_mean(tf.square(error))
  def grad(model, inputs, targets):
  