message: [tf.data] Delete unused code left over from `tf.contrib.data.batch_and_drop_remainder()`.
diff: @@ -31,7 +31,6 @@ from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  -from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import control_flow_ops
  @@ -439,48 +438,6 @@ def unbatch():
  return _apply_fn
  -def _filter_irregular_batches(batch_size):
  - """Transformation that filters out batches that are not of size batch_size."""
  -
  - def _apply_fn(dataset):
  - """Function from `Dataset` to `Dataset` that applies the transformation."""
  - tensor_batch_size = ops.convert_to_tensor(
  - batch_size, dtype=dtypes.int64, name="batch_size")
  -
  - flattened = _RestructuredDataset(
  - dataset,
  - tuple(nest.flatten(dataset.output_types)),
  - output_classes=tuple(nest.flatten(dataset.output_classes)))
  -
  - def _predicate(*xs):
  - """Return `True` if this element is a full batch."""
  - # Extract the dynamic batch size from the first component of the flattened
  - # batched element.
  - first_component = xs[0]
  - first_component_batch_size = array_ops.shape(
  - first_component, out_type=dtypes.int64)[0]
  -
  - return math_ops.equal(first_component_batch_size, tensor_batch_size)
  -
  - filtered = flattened.filter(_predicate)
  -
  - maybe_constant_batch_size = tensor_util.constant_value(tensor_batch_size)
  -
  - def _set_first_dimension(shape):
  - return shape.merge_with(
  - tensor_shape.vector(maybe_constant_batch_size).concatenate(shape[1:]))
  -
  - known_shapes = nest.map_structure(_set_first_dimension,
  - dataset.output_shapes)
  - return _RestructuredDataset(
  - filtered,
  - dataset.output_types,
  - known_shapes,
  - output_classes=dataset.output_classes)
  -
  - return _apply_fn
  -
  -
  @deprecation.deprecated(
  None, "Use `tf.data.Dataset.batch(..., drop_remainder=True)`.")
  def batch_and_drop_remainder(batch_size):
  