message: Removed TODOs that are obsolete.
diff: @@ -802,17 +802,17 @@ class Model(Network):
  # Validates `steps` argument right at the beginning since we use it to
  # construct the dataset object.
  - # TODO(anjalisridhar): This may not be a valid error since we now accept
  - # numpy array inputs. We still want to assert that we have a populated steps
  - # parameter.
  - if check_steps:
  - if steps is None:
  - raise ValueError('When using DistributionStrategy, '
  + # TODO(anjalisridhar): Remove this check once we refactor the
  + # _standardize_user_data code path. This check is already present elsewhere
  + # in the codebase.
  + if check_steps and isinstance(x, dataset_ops.Dataset) and steps is None:
  + raise ValueError('When using Datasets as input, '
  'you should specify the `{steps_name}` argument.'
  .format(steps_name=steps_name))
  first_x_value = nest.flatten(x)[0]
  if isinstance(first_x_value, np.ndarray):
  + assert steps is not None
  x_shape = first_x_value.shape
  if batch_size is None:
  batch_size = distributed_training_utils.get_batch_size(
  @@ -827,8 +827,11 @@ class Model(Network):
  self._distribution_strategy, y)
  x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y))
  - # TODO(anjalisridhar): What should the buffer size be?
  - x = x.shuffle(10000)
  + # 1024 is a good buffer size since it is much larger than the average
  + # batch size provided by the user and provides sufficient randomness.
  + # One thing to keep in mind is the memory usage based on the size of
  + # each sample.
  + x = x.shuffle(1024)
  x = x.repeat()
  x = x.batch(batch_size, drop_remainder=drop_remainder)
  y = None
  @@ -841,9 +844,6 @@ class Model(Network):
  x = x.repeat()
  x = x.batch(batch_size, drop_remainder=drop_remainder)
  - # TODO(anjalisridhar): Can we use the iterator and getnext op cache?
  - # We require users to pass Datasets since we distribute the dataset across
  - # multiple devices.
  assert isinstance(x, dataset_ops.Dataset)
  # TODO(anjalisridhar): We want distribute_dataset() to accept a Dataset or a
  