message: Update docstrings for APIs that raise a RuntimeError when wrapped in
  `tf.function`.
diff: @@ -1020,7 +1020,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  and validation metrics values (if applicable).
  Raises:
  - RuntimeError: If the model was never compiled.
  + RuntimeError: 1. If the model was never compiled or,
  + 2. If `model.fit` is wrapped in `tf.function`.
  +
  ValueError: In case of mismatch between the provided input data
  and what the model expects.
  """
  @@ -1306,6 +1308,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  the display labels for the scalar outputs.
  Raises:
  + RuntimeError: If `model.evaluate` is wrapped in `tf.function`.
  ValueError: in case of invalid arguments.
  """
  _keras_api_gauge.get_cell('evaluate').set(True)
  @@ -1527,6 +1530,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  Numpy array(s) of predictions.
  Raises:
  + RuntimeError: If `model.predict` is wrapped in `tf.function`.
  ValueError: In case of mismatch between the provided
  input data and the model's expectations,
  or in case a stateful model receives a number of samples
  @@ -1654,6 +1658,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  the display labels for the scalar outputs.
  Raises:
  + RuntimeError: If `model.train_on_batch` is wrapped in `tf.function`.
  ValueError: In case of invalid user-provided arguments.
  """
  self._assert_compile_was_called()
  @@ -1714,6 +1719,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  the display labels for the scalar outputs.
  Raises:
  + RuntimeError: If `model.test_on_batch` is wrapped in `tf.function`.
  ValueError: In case of invalid user-provided arguments.
  """
  self._assert_compile_was_called()
  @@ -1748,6 +1754,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  Numpy array(s) of predictions.
  Raises:
  + RuntimeError: If `model.predict_on_batch` is wrapped in `tf.function`.
  ValueError: In case of mismatch between given number of inputs and
  expectations of the model.
  """
  