text: # Copyright 2017 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.
  # ==============================================================================
  """Ignore_errors dataset transformations."""
  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.contrib.data.python.ops import gen_dataset_ops
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import nest
  from tensorflow.python.data.util import sparse
  
  
  def ignore_errors():
    """Creates a `Dataset` from another `Dataset` and silently ignores any errors.
  
    Use this transformation to produce a dataset that contains the same elements
    as the input, but silently drops any elements that caused an error. For
    example:
  
    ```python
    dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
  
    # Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
    dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
  
    # Using `ignore_errors()` will drop the element that causes an error.
    dataset =
        dataset.apply(tf.contrib.data.ignore_errors())  # ==> { 1., 0.5, 0.2 }
    ```
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      @{tf.data.Dataset.apply}.
    """
  
    def _apply_fn(dataset):
      return IgnoreErrorsDataset(dataset)
  
    return _apply_fn
  
  
  class IgnoreErrorsDataset(dataset_ops.Dataset):
    """A `Dataset` that silently ignores errors when computing its input."""
  
    def __init__(self, input_dataset):
      """See `Dataset.ignore_errors()` for details."""
      super(IgnoreErrorsDataset, self).__init__()
      self._input_dataset = input_dataset
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.ignore_errors_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          output_shapes=nest.flatten(
              sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
          output_types=nest.flatten(
              sparse.as_dense_types(self.output_types, self.output_classes)))
  
    @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
  