text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Experimental API for optimizing `tf.data` pipelines."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.data.python.ops import contrib_op_loader  # pylint: disable=unused-import
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_dataset_ops
  
  
  def optimize(optimizations=None):
    """A transformation that applies optimizations.
  
    Args:
      optimizations: (Optional.) A `tf.string` vector `tf.Tensor` identifying
        optimizations to use. If not specified, the default set of optimizations
        is applied.
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      @{tf.data.Dataset.apply}.
    """
  
    def _apply_fn(dataset):
      """Function from `Dataset` to `Dataset` that applies the transformation."""
      return OptimizeDataset(dataset, optimizations)
  
    return _apply_fn
  
  
  class OptimizeDataset(dataset_ops.Dataset):
    """A `Dataset` that acts as an identity, and applies optimizations."""
  
    def __init__(self, input_dataset, optimizations):
      """See `optimize()` for details."""
      super(OptimizeDataset, self).__init__()
      self._input_dataset = input_dataset
      if optimizations is None:
        optimizations = []
      self._optimizations = ops.convert_to_tensor(
          optimizations, dtype=dtypes.string, name="optimizations")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.optimize_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._optimizations,
          **dataset_ops.flat_structure(self))
    @property
    def output_classes(self):
      return self._input_dataset.output_classes
  
    @property
    def output_shapes(self):
      return self._input_dataset.output_shapes
  
    @property
    def output_types(self):
      return self._input_dataset.output_types
  