message: PR Raise error when calling .fit() w/ batch_size and a tf dataset
  Imported from GitHub PR
  Copybara import of the project:
  by Jonah Kohn
  Raise error when calling .fit() w/ batch_size and a tf dataset
diff: @@ -681,7 +681,6 @@ class DatasetAdapter(DataAdapter):
  y=None,
  sample_weights=None,
  steps=None,
  - batch_size=None,
  **kwargs):
  super(DatasetAdapter, self).__init__(x, y, **kwargs)
  # Note that the dataset instance is immutable, its fine to reuse the user
  @@ -691,7 +690,7 @@ class DatasetAdapter(DataAdapter):
  # The user-provided steps.
  self._user_steps = steps
  - self._validate_args(y, sample_weights, steps, batch_size)
  + self._validate_args(y, sample_weights, steps)
  def get_dataset(self):
  return self._dataset
  @@ -720,7 +719,7 @@ class DatasetAdapter(DataAdapter):
  return (self._user_steps is None or
  cardinality.cardinality(self._dataset).numpy() == self._user_steps)
  - def _validate_args(self, y, sample_weights, steps, batch_size):
  + def _validate_args(self, y, sample_weights, steps):
  """Validates `__init__` arguments."""
  # Arguments that shouldn't be passed.
  if not is_none_or_empty(y):
  @@ -730,10 +729,6 @@ class DatasetAdapter(DataAdapter):
  raise ValueError("`sample_weight` argument is not supported when using "
  "dataset as input.")
  - if batch_size is not None:
  - raise ValueError("`batch_size` argument must not be specified when "
  - "using dataset as input.")
  -
  if steps is None:
  if _is_distributed_dataset(self._dataset):
  raise ValueError("When providing a distributed dataset, you must "
  