def testSerializeDeserialize(self):
   test_cases = (
       (),
       sparse_tensor.SparseTensor(
           indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
       sparse_tensor.SparseTensor(
           indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
       sparse_tensor.SparseTensor(
           indices=[[0, 0], [3, 4]], values=[1, -1], dense_shape=[4, 5]),
       (sparse_tensor.SparseTensor(
           indices=[[0, 0]], values=[1], dense_shape=[1, 1])),
       (sparse_tensor.SparseTensor(
           indices=[[0, 0]], values=[1], dense_shape=[1, 1]), ()),
       ((), sparse_tensor.SparseTensor(
           indices=[[0, 0]], values=[1], dense_shape=[1, 1])),
   )
   for expected in test_cases:
     classes = sparse.get_classes(expected)
     shapes = nest.map_structure(lambda _: tensor_shape.TensorShape(None),
                                 classes)
     types = nest.map_structure(lambda _: dtypes.int32, classes)
     actual = sparse.deserialize_sparse_tensors(
         sparse.serialize_sparse_tensors(expected), types, shapes,
         sparse.get_classes(expected))
     nest.assert_same_structure(expected, actual)
     for a, e in zip(nest.flatten(actual), nest.flatten(expected)):
       self.assertSparseValuesEqual(a, e)

  def from_string_handle(string_handle, output_types, output_shapes=None):
    """Creates a new, uninitialized `Iterator` based on the given handle.

    This method allows you to define a "feedable" iterator where you can choose
    between concrete iterators by feeding a value in a @{tf.Session.run} call.
    In that case, `string_handle` would a @{tf.placeholder}, and you would feed
    it with the value of @{tf.data.Iterator.string_handle} in each step.

    For example, if you had two iterators that marked the current position in
    a training dataset and a test dataset, you could choose which to use in
    each step as follows:

    ```python
    train_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    train_iterator_handle = sess.run(train_iterator.string_handle())

    test_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    test_iterator_handle = sess.run(test_iterator.string_handle())

    handle = tf.placeholder(tf.string, shape=[])
    iterator = tf.data.Iterator.from_string_handle(
        handle, train_iterator.output_types)

    next_element = iterator.get_next()
    loss = f(next_element)

    train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
    test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
    ```

    Args:
      string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates
        to a handle produced by the `Iterator.string_handle()` method.
      output_types: A nested structure of `tf.DType` (or `tf.data.SparseType`)
        objects corresponding to each `tf.Tensor` (or `tf.SparseTensor`)
        component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.

    Returns:
      An `Iterator`.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)
    nest.assert_same_structure(output_types, output_shapes)
    string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
    iterator_resource = gen_dataset_ops.iterator_from_string_handle(
        string_handle,
        output_types=nest.flatten(sparse.unwrap_sparse_types(output_types)),
        output_shapes=nest.flatten(output_shapes))
    return Iterator(iterator_resource, None, output_types, output_shapes)

  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)

  def __init__(self, input_dataset, num_workers):
    self._input_dataset = input_dataset

    def recalculate_output_shapes(output_shapes):
      """Recalculates the output_shapes after dividing it by num_workers."""
      if len(output_shapes) < 1:
        raise ValueError("Input shape should have at least one dimension.")
      if (tensor_shape.dimension_value(output_shapes[0]) and
          tensor_shape.dimension_value(output_shapes[0]) % num_workers != 0):
        raise errors.InvalidArgumentError(
            None, None,
            "First dim of input shape: %d is not divisible by num_workers: %d" %
            (output_shapes[0], num_workers))
      output_dims = [d for d in output_shapes.dims]
      output_dims[0] = output_dims[0] // num_workers
      return tensor_shape.TensorShape(output_dims)

    input_types = dataset_ops.get_legacy_output_types(self._input_dataset)
    input_shapes = dataset_ops.get_legacy_output_shapes(self._input_dataset)
    input_classes = dataset_ops.get_legacy_output_classes(self._input_dataset)
    output_shapes = nest.map_structure(recalculate_output_shapes, input_shapes)

    self._structure = structure.convert_legacy_structure(
        input_types, output_shapes, input_classes)
    variant_tensor = ged_ops.experimental_rebatch_dataset(
        self._input_dataset._variant_tensor,  # pylint: disable=protected-access
        num_workers=num_workers,
        **dataset_ops.flat_structure(self))
    super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)

  def __init__(self, dataset, output_types, output_shapes=None):
    """Creates a new dataset with the given output types and shapes.

    The given `dataset` must have a structure that is convertible:
    * `dataset.output_types` must be the same as `output_types` module nesting.
    * Each shape in `dataset.output_shapes` must be compatible with each shape
      in `output_shapes` (if given).

    Note: This helper permits "unsafe casts" for shapes, equivalent to using
    `tf.Tensor.set_shape()` where domain-specific knowledge is available.

    Args:
      dataset: A `Dataset` object.
      output_types: A nested structure of `tf.DType` objects.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects.
        If omitted, the shapes will be inherited from `dataset`.

    Raises:
      ValueError: If either `output_types` or `output_shapes` is not compatible
        with the structure of `dataset`.
    """
    super(_RestructuredDataset, self).__init__()
    self._dataset = dataset

    # Validate that the types are compatible.
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    flat_original_types = nest.flatten(dataset.output_types)
    flat_new_types = nest.flatten(output_types)
    if flat_original_types != flat_new_types:
      raise ValueError(
          "Dataset with output types %r cannot be restructured to have output "
          "types %r" % (dataset.output_types, output_types))

    self._output_types = output_types

    if output_shapes is None:
      # Inherit shapes from the original `dataset`.
      self._output_shapes = nest.pack_sequence_as(output_types,
                                                  nest.flatten(
                                                      dataset.output_shapes))
    else:
      # Validate that the shapes are compatible.
      nest.assert_same_structure(output_types, output_shapes)
      flat_original_shapes = nest.flatten(dataset.output_shapes)
      flat_new_shapes = nest.flatten_up_to(output_types, output_shapes)

      for original_shape, new_shape in zip(flat_original_shapes,
                                           flat_new_shapes):
        if not original_shape.is_compatible_with(new_shape):
          raise ValueError(
              "Dataset with output shapes %r cannot be restructured to have "
              "incompatible output shapes %r" % (dataset.output_shapes,
                                                 output_shapes))
      self._output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)

 def __init__(self, variant_tensor, output_shapes, output_types,
              output_classes):
   # TODO(b/110122868): Consolidate the structure validation logic with the
   # similar logic in `Iterator.from_structure()` and
   # `Dataset.from_generator()`.
   output_types = nest.map_structure(dtypes.as_dtype, output_types)
   output_shapes = nest.map_structure_up_to(
       output_types, tensor_shape.as_shape, output_shapes)
   nest.assert_same_structure(output_types, output_shapes)
   nest.assert_same_structure(output_types, output_classes)
   self._variant_tensor = variant_tensor
   self._output_shapes = output_shapes
   self._output_types = output_types
   self._output_classes = output_classes

  def __init__(self, driver_name, data_source_name, query, output_types):
    """Creates a `SqlDataset`.

    `SqlDataset` allows a user to read data from the result set of a SQL query.
    For example:

    ```python
    tf.enable_eager_execution()

    dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3",
                                              "SELECT name, age FROM people",
                                              (tf.string, tf.int32))
    # Prints the rows of the result set of the above query.
    for element in dataset:
      print(element)
    ```

    Args:
      driver_name: A 0-D `tf.string` tensor containing the database type.
        Currently, the only supported value is 'sqlite'.
      data_source_name: A 0-D `tf.string` tensor containing a connection string
        to connect to the database.
      query: A 0-D `tf.string` tensor containing the SQL query to execute.
      output_types: A tuple of `tf.DType` objects representing the types of the
        columns returned by `query`.
    """
    self._driver_name = ops.convert_to_tensor(
        driver_name, dtype=dtypes.string, name="driver_name")
    self._data_source_name = ops.convert_to_tensor(
        data_source_name, dtype=dtypes.string, name="data_source_name")
    self._query = ops.convert_to_tensor(
        query, dtype=dtypes.string, name="query")
    self._structure = structure.NestedStructure(
        nest.map_structure(
            lambda dtype: structure.TensorStructure(dtype, []), output_types))
    variant_tensor = gen_experimental_dataset_ops.experimental_sql_dataset(
        self._driver_name, self._data_source_name, self._query,
        **dataset_ops.flat_structure(self))
    super(SqlDatasetV2, self).__init__(variant_tensor)

  def __init__(self, input_dataset):
    """See `unbatch()` for more details."""
    flat_shapes = nest.flatten(input_dataset.output_shapes)
    if any(s.ndims == 0 for s in flat_shapes):
      raise ValueError("Cannot unbatch an input with scalar components.")
    known_batch_dim = tensor_shape.Dimension(None)
    for s in flat_shapes:
      try:
        known_batch_dim = known_batch_dim.merge_with(s[0])
      except ValueError:
        raise ValueError("Cannot unbatch an input whose components have "
                         "different batch sizes.")
    self._input_dataset = input_dataset

    self._structure = structure.convert_legacy_structure(
        input_dataset.output_types,
        nest.map_structure(lambda s: s[1:], input_dataset.output_shapes),
        input_dataset.output_classes)

    variant_tensor = ged_ops.experimental_unbatch_dataset(
        self._input_dataset._variant_tensor,  # pylint: disable=protected-access
        **dataset_ops.flat_structure(self))
    super(_UnbatchDataset, self).__init__(input_dataset, variant_tensor)

 def __init__(self, input_dataset, batch_size, padded_shapes, padding_values):
   """Initialize `PrependFromQueueAndPaddedBatchDataset`."""
   super(_PrependFromQueueAndPaddedBatchDataset, self).__init__()
   if sparse.any_sparse(input_dataset.output_classes):
     raise TypeError(
         "Batching of padded sparse tensors is not currently supported")
   self._input_dataset = input_dataset
   self._batch_size = ops.convert_to_tensor(
       batch_size, dtype=dtypes.int64, name="batch_size")
   # pylint: disable=protected-access
   if padded_shapes is None:
     self._padded_shapes = nest.map_structure(
         dataset_ops._partial_shape_to_tensor, input_dataset.output_shapes)
   else:
     self._padded_shapes = nest.map_structure_up_to(
         input_dataset.output_shapes, dataset_ops._partial_shape_to_tensor,
         padded_shapes)
   padding_values = (
       padding_values if padding_values is not None else
       dataset_ops._default_padding(input_dataset))
   self._padding_values = nest.map_structure_up_to(
       input_dataset.output_shapes, dataset_ops._padding_value_to_tensor,
       padding_values, input_dataset.output_types)

 def output_shapes(self):
   return nest.map_structure(lambda _: tensor_shape.TensorShape([]),
                             self._output_types)

 def output_classes(self):
   return nest.map_structure(lambda _: ops.Tensor, self._output_types)

  def testMapStructure(self):
    structure1 = (((1, 2), 3), 4, (5, 6))
    structure2 = (((7, 8), 9), 10, (11, 12))
    structure1_plus1 = nest.map_structure(lambda x: x + 1, structure1)
    nest.assert_same_structure(structure1, structure1_plus1)
    self.assertAllEqual(
        [2, 3, 4, 5, 6, 7],
        nest.flatten(structure1_plus1))
    structure1_plus_structure2 = nest.map_structure(
        lambda x, y: x + y, structure1, structure2)
    self.assertEqual(
        (((1 + 7, 2 + 8), 3 + 9), 4 + 10, (5 + 11, 6 + 12)),
        structure1_plus_structure2)

    self.assertEqual(3, nest.map_structure(lambda x: x - 1, 4))

    self.assertEqual(7, nest.map_structure(lambda x, y: x + y, 3, 4))

    with self.assertRaisesRegexp(TypeError, "callable"):
      nest.map_structure("bad", structure1_plus1)

    with self.assertRaisesRegexp(ValueError, "same nested structure"):
      nest.map_structure(lambda x, y: None, 3, (3,))

    with self.assertRaisesRegexp(TypeError, "same sequence type"):
      nest.map_structure(lambda x, y: None, ((3, 4), 5), {"a": (3, 4), "b": 5})

    with self.assertRaisesRegexp(ValueError, "same nested structure"):
      nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)))

    with self.assertRaisesRegexp(ValueError, "same nested structure"):
      nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)),
                         check_types=False)

    with self.assertRaisesRegexp(ValueError, "Only valid keyword argument"):
      nest.map_structure(lambda x: None, structure1, foo="a")

    with self.assertRaisesRegexp(ValueError, "Only valid keyword argument"):
      nest.map_structure(lambda x: None, structure1, check_types=False, foo="a")

 def _unbatch(self):
   return NestedStructure(nest.map_structure(
       lambda s: s._unbatch(), self._nested_structure))

 def output_shapes(self):
   return nest.map_structure(lambda s: s[1:],
                             self._input_dataset.output_shapes)

 def output_shapes(self):
   # First output is a variant representing the Queue
   return (tensor_shape.vector(None),
           nest.map_structure(self._as_batch_shape, self._padded_shapes))

  def from_structure(output_types,
                     output_shapes=None,
                     shared_name=None,
                     output_classes=None):
    """Creates a new, uninitialized `Iterator` with the given structure.

    This iterator-constructing method can be used to create an iterator that
    is reusable with many different datasets.

    The returned iterator is not bound to a particular dataset, and it has
    no `initializer`. To initialize the iterator, run the operation returned by
    `Iterator.make_initializer(dataset)`.

    The following is an example

    ```python
    iterator = Iterator.from_structure(tf.int64, tf.TensorShape([]))

    dataset_range = Dataset.range(10)
    range_initializer = iterator.make_initializer(dataset_range)

    dataset_evens = dataset_range.filter(lambda x: x % 2 == 0)
    evens_initializer = iterator.make_initializer(dataset_evens)

    # Define a model based on the iterator; in this example, the model_fn
    # is expected to take scalar tf.int64 Tensors as input (see
    # the definition of 'iterator' above).
    prediction, loss = model_fn(iterator.get_next())

    # Train for `num_epochs`, where for each epoch, we first iterate over
    # dataset_range, and then iterate over dataset_evens.
    for _ in range(num_epochs):
      # Initialize the iterator to `dataset_range`
      sess.run(range_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break

      # Initialize the iterator to `dataset_evens`
      sess.run(evens_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break
    ```

    Args:
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.
      shared_name: (Optional.) If non-empty, this iterator will be shared under
        the given name across multiple sessions that share the same devices
        (e.g. when using a remote server).
      output_classes: (Optional.) A nested structure of Python `type` objects
        corresponding to each component of an element of this iterator. If
        omitted, each component is assumed to be of type `tf.Tensor`.

    Returns:
      An `Iterator`.

    Raises:
      TypeError: If the structures of `output_shapes` and `output_types` are
        not the same.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)
    if output_classes is None:
      output_classes = nest.map_structure(lambda _: ops.Tensor, output_types)
    nest.assert_same_structure(output_types, output_shapes)
    if shared_name is None:
      shared_name = ""
    iterator_resource = gen_dataset_ops.iterator(
        container="",
        shared_name=shared_name,
        output_types=nest.flatten(
            sparse.as_dense_types(output_types, output_classes)),
        output_shapes=nest.flatten(
            sparse.as_dense_shapes(output_shapes, output_classes)))
    return Iterator(iterator_resource, None, output_types, output_shapes,
                    output_classes)

  def __init__(self,
               dataset,
               output_types,
               output_shapes=None,
               output_classes=None,
               allow_unsafe_cast=False):
    """Creates a new dataset with the given output types and shapes.

    The given `dataset` must have a structure that is convertible:
    * `dataset.output_types` must be the same as `output_types` module nesting.
    * Each shape in `dataset.output_shapes` must be compatible with each shape
      in `output_shapes` (if given).

    Note: This helper permits "unsafe casts" for shapes, equivalent to using
    `tf.Tensor.set_shape()` where domain-specific knowledge is available.

    Args:
      dataset: A `Dataset` object.
      output_types: A nested structure of `tf.DType` objects.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects.
        If omitted, the shapes will be inherited from `dataset`.
      output_classes: (Optional.) A nested structure of class types.
        If omitted, the class types will be inherited from `dataset`.
      allow_unsafe_cast: (Optional.) If `True`, the caller may switch the
        reported output types and shapes of the restructured dataset, e.g. to
        switch a sparse tensor represented as `tf.variant` to its user-visible
        type and shape.

    Raises:
      ValueError: If either `output_types` or `output_shapes` is not compatible
        with the structure of `dataset`.
    """
    self._input_dataset = dataset

    input_types = dataset_ops.get_legacy_output_types(dataset)
    if not allow_unsafe_cast:
      # Validate that the types are compatible.
      output_types = nest.map_structure(dtypes.as_dtype, output_types)
      flat_original_types = nest.flatten(input_types)
      flat_new_types = nest.flatten(output_types)
      if flat_original_types != flat_new_types:
        raise ValueError(
            "Dataset with output types %r cannot be restructured to have "
            "output types %r" %
            (dataset_ops.get_legacy_output_types(dataset), output_types))

    input_shapes = dataset_ops.get_legacy_output_shapes(dataset)
    if output_shapes is None:
      # Inherit shapes from the original `dataset`.
      output_shapes = nest.pack_sequence_as(
          output_types, nest.flatten(input_shapes))
    else:
      if not allow_unsafe_cast:
        # Validate that the shapes are compatible.
        nest.assert_same_structure(output_types, output_shapes)
        flat_original_shapes = nest.flatten(input_shapes)
        flat_new_shapes = nest.flatten_up_to(output_types, output_shapes)

        for original_shape, new_shape in zip(flat_original_shapes,
                                             flat_new_shapes):
          if not original_shape.is_compatible_with(new_shape):
            raise ValueError(
                "Dataset with output shapes %r cannot be restructured to have "
                "incompatible output shapes %r" % (input_shapes,
                                                   output_shapes))
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)

    input_classes = dataset_ops.get_legacy_output_classes(dataset)
    if output_classes is None:
      # Inherit class types from the original `dataset`.
      output_classes = nest.pack_sequence_as(
          output_types, nest.flatten(input_classes))

    self._structure = structure.convert_legacy_structure(
        output_types, output_shapes, output_classes)
    variant_tensor = self._input_dataset._variant_tensor  # pylint: disable=protected-access
    super(_RestructuredDataset, self).__init__(dataset, variant_tensor)

  def from_string_handle(string_handle,
                         output_types,
                         output_shapes=None,
                         output_classes=None):
    """Creates a new, uninitialized `Iterator` based on the given handle.

    This method allows you to define a "feedable" iterator where you can choose
    between concrete iterators by feeding a value in a `tf.Session.run` call.
    In that case, `string_handle` would be a `tf.placeholder`, and you would
    feed it with the value of `tf.data.Iterator.string_handle` in each step.

    For example, if you had two iterators that marked the current position in
    a training dataset and a test dataset, you could choose which to use in
    each step as follows:

    ```python
    train_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    train_iterator_handle = sess.run(train_iterator.string_handle())

    test_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    test_iterator_handle = sess.run(test_iterator.string_handle())

    handle = tf.placeholder(tf.string, shape=[])
    iterator = tf.data.Iterator.from_string_handle(
        handle, train_iterator.output_types)

    next_element = iterator.get_next()
    loss = f(next_element)

    train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
    test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
    ```

    Args:
      string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates
        to a handle produced by the `Iterator.string_handle()` method.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.
      output_classes: (Optional.) A nested structure of Python `type` objects
        corresponding to each component of an element of this iterator. If
        omitted, each component is assumed to be of type `tf.Tensor`.

    Returns:
      An `Iterator`.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)
    if output_classes is None:
      output_classes = nest.map_structure(lambda _: ops.Tensor, output_types)
    nest.assert_same_structure(output_types, output_shapes)
    output_structure = structure_lib.convert_legacy_structure(
        output_types, output_shapes, output_classes)
    string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
    # pylint: disable=protected-access
    if compat.forward_compatible(2018, 8, 3):
      if _device_stack_is_empty():
        with ops.device("/cpu:0"):
          iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
              string_handle,
              output_types=output_structure._flat_types,
              output_shapes=output_structure._flat_shapes)
      else:
        iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
            string_handle,
            output_types=output_structure._flat_types,
            output_shapes=output_structure._flat_shapes)
    else:
      iterator_resource = gen_dataset_ops.iterator_from_string_handle(
          string_handle,
          output_types=output_structure._flat_types,
          output_shapes=output_structure._flat_shapes)
    # pylint: enable=protected-access
    return Iterator(iterator_resource, None, output_types, output_shapes,
                    output_classes)

 def _batch(self, batch_size):
   return NestedStructure(nest.map_structure(
       lambda s: s._batch(batch_size), self._nested_structure))

  def from_generator(generator, output_types, output_shapes=None):
    """Creates a `Dataset` whose elements are generated by `generator`.

    The `generator` argument must be a callable object that returns
    an object that support the `iter()` protocol (e.g. a generator function).
    The elements generated by `generator` must be compatible with the given
    `output_types` and (optional) `output_shapes` arguments.

    For example:

    ```python
    import itertools

    def gen():
      for i in itertools.count(1):
        yield (i, [1] * i)

    ds = Dataset.from_generator(
        gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))
    value = ds.make_one_shot_iterator().get_next()

    sess.run(value)  # (1, array([1]))
    sess.run(value)  # (2, array([1, 1]))
    ```

    Args:
      generator: A callable object that takes no arguments and returns an
        object that supports the `iter()` protocol.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element yielded by `generator`.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape`
        objects corresponding to each component of an element yielded by
        `generator`.

    Returns:
      A `Dataset`.
    """
    if not callable(generator):
      raise TypeError("`generator` must be callable.")
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)

    flattened_types = nest.flatten(output_types)
    flattened_shapes = nest.flatten(output_shapes)

    generator_state = dataset_ops.Dataset._GeneratorState(generator)

    def get_iterator_id_map_fn(unused_dummy):
      """Creates a unique `iterator_id` for each pass over the dataset.

      The "iterator_id" disambiguates between multiple concurrently
      existing iterators.

      Args:
        unused_dummy: Ignored value.

      Returns:
        A `tf.int64` tensor whose value uniquely identifies an iterator in
        `generator_state`.
      """
      return script_ops.py_func(
          generator_state.get_next_id, [], dtypes.int64, stateful=True)

    def generator_map_fn(iterator_id_t):
      """Generates the next element from iterator with ID `iterator_id_t`.

      We map this function across an infinite repetition of the
      `iterator_id_t`, and raise `StopIteration` to terminate the iteration.

      Args:
        iterator_id_t: A `tf.int64` tensor whose value uniquely identifies
          the iterator in `generator_state` from which to generate an element.

      Returns:
        A nested structure of tensors representing an element from the iterator.
      """

      def generator_py_func(iterator_id):
        """A `py_func` that will be called to invoke the iterator."""
        try:
          values = next(generator_state.get_iterator(iterator_id))
        except StopIteration:
          generator_state.iterator_completed(iterator_id)
          raise StopIteration("Iteration finished.")

        # Use the same _convert function from the py_func() implementation to
        # convert the returned values to arrays early, so that we can inspect
        # their values.
        # pylint: disable=protected-access
        ret_arrays = [
            script_ops.FuncRegistry._convert(ret, dtype=dtype.as_numpy_dtype)
            for ret, dtype in zip(nest.flatten_up_to(output_types, values),
                                  flattened_types)
        ]
        # pylint: enable=protected-access

#.........这里部分代码省略.........