"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
Original C++ source file: dataset_ops.cc
"""

import collections as _collections
import six as _six

from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util.tf_export import tf_export


def anonymous_iterator(output_types, output_shapes, name=None):
  r"""A container for an iterator resource.

  Args:
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'anonymous_iterator' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'anonymous_iterator' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "AnonymousIterator", output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "AnonymousIterator", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "AnonymousIterator", name, _ctx._post_execution_callbacks,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return anonymous_iterator_eager_fallback(
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def anonymous_iterator_eager_fallback(output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function anonymous_iterator
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'anonymous_iterator' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'anonymous_iterator' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _inputs_flat = []
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"AnonymousIterator", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "AnonymousIterator", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def batch_dataset(input_dataset, batch_size, output_types, output_shapes, name=None):
  r"""Creates a dataset that batches `batch_size` elements from `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'batch_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'batch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "BatchDataset", input_dataset=input_dataset, batch_size=batch_size,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "BatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "BatchDataset",
        name, _ctx._post_execution_callbacks, input_dataset, batch_size,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return batch_dataset_eager_fallback(
          input_dataset, batch_size, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def batch_dataset_eager_fallback(input_dataset, batch_size, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function batch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'batch_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'batch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  _inputs_flat = [input_dataset, batch_size]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"BatchDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "BatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def batch_dataset_v2(input_dataset, batch_size, drop_remainder, output_types, output_shapes, name=None):
  r"""Creates a dataset that batches `batch_size` elements from `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a batch.
    drop_remainder: A `Tensor` of type `bool`.
      A scalar representing whether the last batch should be dropped in case its size
      is smaller than desired.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'batch_dataset_v2' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'batch_dataset_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "BatchDatasetV2", input_dataset=input_dataset, batch_size=batch_size,
        drop_remainder=drop_remainder, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "BatchDatasetV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "BatchDatasetV2", name, _ctx._post_execution_callbacks, input_dataset,
        batch_size, drop_remainder, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return batch_dataset_v2_eager_fallback(
          input_dataset, batch_size, drop_remainder,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def batch_dataset_v2_eager_fallback(input_dataset, batch_size, drop_remainder, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function batch_dataset_v2
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'batch_dataset_v2' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'batch_dataset_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
  _inputs_flat = [input_dataset, batch_size, drop_remainder]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"BatchDatasetV2", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "BatchDatasetV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def bytes_produced_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None):
  r"""Records the bytes size of each element of `input_dataset` in a StatsAggregator.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    tag: A `Tensor` of type `string`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'bytes_produced_stats_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'bytes_produced_stats_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "BytesProducedStatsDataset", input_dataset=input_dataset, tag=tag,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "BytesProducedStatsDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "BytesProducedStatsDataset", name, _ctx._post_execution_callbacks,
        input_dataset, tag, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return bytes_produced_stats_dataset_eager_fallback(
          input_dataset, tag, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def bytes_produced_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function bytes_produced_stats_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'bytes_produced_stats_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'bytes_produced_stats_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  tag = _ops.convert_to_tensor(tag, _dtypes.string)
  _inputs_flat = [input_dataset, tag]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"BytesProducedStatsDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "BytesProducedStatsDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def cache_dataset(input_dataset, filename, output_types, output_shapes, name=None):
  r"""Creates a dataset that caches elements from `input_dataset`.

  A CacheDataset will iterate over the input_dataset, and store tensors. If the
  cache already exists, the cache will be used. If the cache is inappropriate
  (e.g. cannot be opened, contains tensors of the wrong shape / size), an error
  will the returned when used.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    filename: A `Tensor` of type `string`.
      A path on the filesystem where we should cache the dataset. Note: this
      will be a directory.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'cache_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'cache_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "CacheDataset", input_dataset=input_dataset, filename=filename,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "CacheDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "CacheDataset",
        name, _ctx._post_execution_callbacks, input_dataset, filename,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return cache_dataset_eager_fallback(
          input_dataset, filename, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def cache_dataset_eager_fallback(input_dataset, filename, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function cache_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'cache_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'cache_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  filename = _ops.convert_to_tensor(filename, _dtypes.string)
  _inputs_flat = [input_dataset, filename]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"CacheDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "CacheDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def concatenate_dataset(input_dataset, another_dataset, output_types, output_shapes, name=None):
  r"""Creates a dataset that concatenates `input_dataset` with `another_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    another_dataset: A `Tensor` of type `variant`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'concatenate_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'concatenate_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ConcatenateDataset", input_dataset=input_dataset,
        another_dataset=another_dataset, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ConcatenateDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ConcatenateDataset", name, _ctx._post_execution_callbacks,
        input_dataset, another_dataset, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return concatenate_dataset_eager_fallback(
          input_dataset, another_dataset, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def concatenate_dataset_eager_fallback(input_dataset, another_dataset, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function concatenate_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'concatenate_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'concatenate_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  another_dataset = _ops.convert_to_tensor(another_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset, another_dataset]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ConcatenateDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ConcatenateDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def dataset_to_graph(input_dataset, name=None):
  r"""Returns a serialized GraphDef representing `input_dataset`.

  Returns a graph representation for `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the dataset to return the graph representation for.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "DatasetToGraph", input_dataset=input_dataset, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "DatasetToGraph", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "DatasetToGraph", name, _ctx._post_execution_callbacks, input_dataset)
      return _result
    except _core._FallbackException:
      return dataset_to_graph_eager_fallback(
          input_dataset, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def dataset_to_graph_eager_fallback(input_dataset, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function dataset_to_graph
  """
  _ctx = ctx if ctx else _context.context()
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset]
  _attrs = None
  _result = _execute.execute(b"DatasetToGraph", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "DatasetToGraph", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def dataset_to_single_element(dataset, output_types, output_shapes, name=None):
  r"""Outputs the single element from the given dataset.

  Args:
    dataset: A `Tensor` of type `variant`.
      A handle to a dataset that contains a single element.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'dataset_to_single_element' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'dataset_to_single_element' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "DatasetToSingleElement", dataset=dataset, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "DatasetToSingleElement", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "DatasetToSingleElement", name, _ctx._post_execution_callbacks,
        dataset, "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return dataset_to_single_element_eager_fallback(
          dataset, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def dataset_to_single_element_eager_fallback(dataset, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function dataset_to_single_element
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'dataset_to_single_element' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'dataset_to_single_element' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  dataset = _ops.convert_to_tensor(dataset, _dtypes.variant)
  _inputs_flat = [dataset]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"DatasetToSingleElement", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "DatasetToSingleElement", _inputs_flat, _attrs, _result, name)
  return _result


def dataset_to_tf_record(input_dataset, filename, compression_type, name=None):
  r"""Writes the given dataset to the given file using the TFRecord format.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the dataset to write.
    filename: A `Tensor` of type `string`.
      A scalar string tensor representing the filename to use.
    compression_type: A `Tensor` of type `string`.
      A scalar string tensor containing either (i) the empty string (no
      compression), (ii) "ZLIB", or (iii) "GZIP".
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "DatasetToTFRecord", input_dataset=input_dataset, filename=filename,
        compression_type=compression_type, name=name)
    return _op
    _result = None
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "DatasetToTFRecord", name, _ctx._post_execution_callbacks,
        input_dataset, filename, compression_type)
      return _result
    except _core._FallbackException:
      return dataset_to_tf_record_eager_fallback(
          input_dataset, filename, compression_type, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def dataset_to_tf_record_eager_fallback(input_dataset, filename, compression_type, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function dataset_to_tf_record
  """
  _ctx = ctx if ctx else _context.context()
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  filename = _ops.convert_to_tensor(filename, _dtypes.string)
  compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string)
  _inputs_flat = [input_dataset, filename, compression_type]
  _attrs = None
  _result = _execute.execute(b"DatasetToTFRecord", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None):
  r"""Creates a dataset that batches input elements into a SparseTensor.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A handle to an input dataset. Must have a single component.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch.
    row_shape: A `Tensor` of type `int64`.
      A vector representing the dense shape of each row in the produced
      SparseTensor. The shape may be partially specified, using `-1` to indicate
      that a particular dimension should use the maximum size of all batch elements.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'dense_to_sparse_batch_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'dense_to_sparse_batch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "DenseToSparseBatchDataset", input_dataset=input_dataset,
        batch_size=batch_size, row_shape=row_shape, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "DenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "DenseToSparseBatchDataset", name, _ctx._post_execution_callbacks,
        input_dataset, batch_size, row_shape, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return dense_to_sparse_batch_dataset_eager_fallback(
          input_dataset, batch_size, row_shape, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def dense_to_sparse_batch_dataset_eager_fallback(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function dense_to_sparse_batch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'dense_to_sparse_batch_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'dense_to_sparse_batch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  row_shape = _ops.convert_to_tensor(row_shape, _dtypes.int64)
  _inputs_flat = [input_dataset, batch_size, row_shape]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"DenseToSparseBatchDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "DenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def deserialize_iterator(resource_handle, serialized, name=None):
  r"""Converts the given variant tensor to an iterator and stores it in the given resource.

  Args:
    resource_handle: A `Tensor` of type `resource`.
      A handle to an iterator resource.
    serialized: A `Tensor` of type `variant`.
      A variant tensor storing the state of the iterator contained in the
      resource.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "DeserializeIterator", resource_handle=resource_handle,
        serialized=serialized, name=name)
    return _op
    _result = None
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "DeserializeIterator", name, _ctx._post_execution_callbacks,
        resource_handle, serialized)
      return _result
    except _core._FallbackException:
      return deserialize_iterator_eager_fallback(
          resource_handle, serialized, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def deserialize_iterator_eager_fallback(resource_handle, serialized, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function deserialize_iterator
  """
  _ctx = ctx if ctx else _context.context()
  resource_handle = _ops.convert_to_tensor(resource_handle, _dtypes.resource)
  serialized = _ops.convert_to_tensor(serialized, _dtypes.variant)
  _inputs_flat = [resource_handle, serialized]
  _attrs = None
  _result = _execute.execute(b"DeserializeIterator", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def enqueue_in_queue_dataset(queue, components, name=None):
  r"""TODO: add doc.

  Args:
    queue: A `Tensor` of type `variant`.
    components: A list of `Tensor` objects.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "EnqueueInQueueDataset", queue=queue, components=components,
        name=name)
    return _op
    _result = None
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "EnqueueInQueueDataset", name, _ctx._post_execution_callbacks, queue,
        components)
      return _result
    except _core._FallbackException:
      return enqueue_in_queue_dataset_eager_fallback(
          queue, components, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def enqueue_in_queue_dataset_eager_fallback(queue, components, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function enqueue_in_queue_dataset
  """
  _ctx = ctx if ctx else _context.context()
  _attr_Tcomponents, components = _execute.convert_to_mixed_eager_tensors(components, _ctx)
  queue = _ops.convert_to_tensor(queue, _dtypes.variant)
  _inputs_flat = [queue] + list(components)
  _attrs = ("Tcomponents", _attr_Tcomponents)
  _result = _execute.execute(b"EnqueueInQueueDataset", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def filter_by_last_component_dataset(input_dataset, output_types, output_shapes, name=None):
  r"""Creates a dataset containing elements of first component of `input_dataset` having true in the last component.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'filter_by_last_component_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'filter_by_last_component_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "FilterByLastComponentDataset", input_dataset=input_dataset,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "FilterByLastComponentDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "FilterByLastComponentDataset", name, _ctx._post_execution_callbacks,
        input_dataset, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return filter_by_last_component_dataset_eager_fallback(
          input_dataset, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def filter_by_last_component_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function filter_by_last_component_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'filter_by_last_component_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'filter_by_last_component_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"FilterByLastComponentDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "FilterByLastComponentDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def filter_dataset(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None):
  r"""Creates a dataset containing elements of `input_dataset` matching `predicate`.

  The `predicate` function must return a scalar boolean and accept the
  following arguments:

  * One tensor for each component of an element of `input_dataset`.
  * One tensor for each value in `other_arguments`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when
      building a closure for `predicate`.
    predicate: A function decorated with @Defun.
      A function returning a scalar boolean.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'filter_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'filter_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "FilterDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, predicate=predicate,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("predicate", _op.get_attr("predicate"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "FilterDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "FilterDataset", name, _ctx._post_execution_callbacks, input_dataset,
        other_arguments, "predicate", predicate, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return filter_dataset_eager_fallback(
          input_dataset, other_arguments, predicate=predicate,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def filter_dataset_eager_fallback(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function filter_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'filter_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'filter_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(other_arguments)
  _attrs = ("predicate", predicate, "Targuments", _attr_Targuments,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"FilterDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "FilterDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def fixed_length_record_dataset(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name=None):
  r"""Creates a dataset that emits the records from one or more binary files.

  Args:
    filenames: A `Tensor` of type `string`.
      A scalar or a vector containing the name(s) of the file(s) to be
      read.
    header_bytes: A `Tensor` of type `int64`.
      A scalar representing the number of bytes to skip at the
      beginning of a file.
    record_bytes: A `Tensor` of type `int64`.
      A scalar representing the number of bytes in each record.
    footer_bytes: A `Tensor` of type `int64`.
      A scalar representing the number of bytes to skip at the end
      of a file.
    buffer_size: A `Tensor` of type `int64`.
      A scalar representing the number of bytes to buffer. Must be > 0.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "FixedLengthRecordDataset", filenames=filenames,
        header_bytes=header_bytes, record_bytes=record_bytes,
        footer_bytes=footer_bytes, buffer_size=buffer_size, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "FixedLengthRecordDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "FixedLengthRecordDataset", name, _ctx._post_execution_callbacks,
        filenames, header_bytes, record_bytes, footer_bytes, buffer_size)
      return _result
    except _core._FallbackException:
      return fixed_length_record_dataset_eager_fallback(
          filenames, header_bytes, record_bytes, footer_bytes, buffer_size,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def fixed_length_record_dataset_eager_fallback(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function fixed_length_record_dataset
  """
  _ctx = ctx if ctx else _context.context()
  filenames = _ops.convert_to_tensor(filenames, _dtypes.string)
  header_bytes = _ops.convert_to_tensor(header_bytes, _dtypes.int64)
  record_bytes = _ops.convert_to_tensor(record_bytes, _dtypes.int64)
  footer_bytes = _ops.convert_to_tensor(footer_bytes, _dtypes.int64)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  _inputs_flat = [filenames, header_bytes, record_bytes, footer_bytes, buffer_size]
  _attrs = None
  _result = _execute.execute(b"FixedLengthRecordDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "FixedLengthRecordDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def flat_map_dataset(input_dataset, other_arguments, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  Unlike MapDataset, the `f` in FlatMapDataset is expected to return a
  Dataset variant, and FlatMapDataset will flatten successive results
  into a single Dataset.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    f: A function decorated with @Defun.
      A function mapping elements of `input_dataset`, concatenated with
      `other_arguments`, to a Dataset variant that contains elements matching
      `output_types` and `output_shapes`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'flat_map_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'flat_map_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "FlatMapDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, f=f, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "FlatMapDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "FlatMapDataset", name, _ctx._post_execution_callbacks, input_dataset,
        other_arguments, "f", f, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return flat_map_dataset_eager_fallback(
          input_dataset, other_arguments, f=f, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def flat_map_dataset_eager_fallback(input_dataset, other_arguments, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function flat_map_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'flat_map_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'flat_map_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(other_arguments)
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"FlatMapDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "FlatMapDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def generator_dataset(init_func_other_args, next_func_other_args, finalize_func_other_args, init_func, next_func, finalize_func, output_types, output_shapes, name=None):
  r"""Creates a dataset that invokes a function to generate elements.

  Args:
    init_func_other_args: A list of `Tensor` objects.
    next_func_other_args: A list of `Tensor` objects.
    finalize_func_other_args: A list of `Tensor` objects.
    init_func: A function decorated with @Defun.
    next_func: A function decorated with @Defun.
    finalize_func: A function decorated with @Defun.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'generator_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'generator_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "GeneratorDataset", init_func_other_args=init_func_other_args,
        next_func_other_args=next_func_other_args,
        finalize_func_other_args=finalize_func_other_args,
        init_func=init_func, next_func=next_func, finalize_func=finalize_func,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("init_func", _op.get_attr("init_func"), "next_func",
              _op.get_attr("next_func"), "finalize_func",
              _op.get_attr("finalize_func"), "Tinit_func_args",
              _op.get_attr("Tinit_func_args"), "Tnext_func_args",
              _op.get_attr("Tnext_func_args"), "Tfinalize_func_args",
              _op.get_attr("Tfinalize_func_args"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "GeneratorDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "GeneratorDataset", name, _ctx._post_execution_callbacks,
        init_func_other_args, next_func_other_args, finalize_func_other_args,
        "init_func", init_func, "next_func", next_func, "finalize_func",
        finalize_func, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return generator_dataset_eager_fallback(
          init_func_other_args, next_func_other_args,
          finalize_func_other_args, init_func=init_func, next_func=next_func,
          finalize_func=finalize_func, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def generator_dataset_eager_fallback(init_func_other_args, next_func_other_args, finalize_func_other_args, init_func, next_func, finalize_func, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function generator_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'generator_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'generator_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Tinit_func_args, init_func_other_args = _execute.convert_to_mixed_eager_tensors(init_func_other_args, _ctx)
  _attr_Tnext_func_args, next_func_other_args = _execute.convert_to_mixed_eager_tensors(next_func_other_args, _ctx)
  _attr_Tfinalize_func_args, finalize_func_other_args = _execute.convert_to_mixed_eager_tensors(finalize_func_other_args, _ctx)
  _inputs_flat = list(init_func_other_args) + list(next_func_other_args) + list(finalize_func_other_args)
  _attrs = ("init_func", init_func, "next_func", next_func, "finalize_func",
  finalize_func, "Tinit_func_args", _attr_Tinit_func_args, "Tnext_func_args",
  _attr_Tnext_func_args, "Tfinalize_func_args", _attr_Tfinalize_func_args,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"GeneratorDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "GeneratorDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None):
  r"""Creates a dataset that computes a group-by on `input_dataset`.

  Creates a dataset that computes a group-by on `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    key_func_other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when
      building a closure for `key_func`.
    init_func_other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when
      building a closure for `init_func`.
    reduce_func_other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when
      building a closure for `reduce_func`.
    finalize_func_other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when
      building a closure for `finalize_func`.
    key_func: A function decorated with @Defun.
      A function mapping an element of `input_dataset`, concatenated
      with `key_func_other_arguments` to a scalar value of type DT_INT64.
    init_func: A function decorated with @Defun.
      A function mapping a key of type DT_INT64, concatenated with
      `init_func_other_arguments` to the initial reducer state.
    reduce_func: A function decorated with @Defun.
      A function mapping the current reducer state and an element of `input_dataset`,
      concatenated with `reduce_func_other_arguments` to a new reducer state.
    finalize_func: A function decorated with @Defun.
      A function mapping the final reducer state to an output element.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'group_by_reducer_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'group_by_reducer_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "GroupByReducerDataset", input_dataset=input_dataset,
        key_func_other_arguments=key_func_other_arguments,
        init_func_other_arguments=init_func_other_arguments,
        reduce_func_other_arguments=reduce_func_other_arguments,
        finalize_func_other_arguments=finalize_func_other_arguments,
        key_func=key_func, init_func=init_func, reduce_func=reduce_func,
        finalize_func=finalize_func, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("key_func", _op.get_attr("key_func"), "init_func",
              _op.get_attr("init_func"), "reduce_func",
              _op.get_attr("reduce_func"), "finalize_func",
              _op.get_attr("finalize_func"), "Tkey_func_other_arguments",
              _op.get_attr("Tkey_func_other_arguments"),
              "Tinit_func_other_arguments",
              _op.get_attr("Tinit_func_other_arguments"),
              "Treduce_func_other_arguments",
              _op.get_attr("Treduce_func_other_arguments"),
              "Tfinalize_func_other_arguments",
              _op.get_attr("Tfinalize_func_other_arguments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "GroupByReducerDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "GroupByReducerDataset", name, _ctx._post_execution_callbacks,
        input_dataset, key_func_other_arguments, init_func_other_arguments,
        reduce_func_other_arguments, finalize_func_other_arguments,
        "key_func", key_func, "init_func", init_func, "reduce_func",
        reduce_func, "finalize_func", finalize_func, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return group_by_reducer_dataset_eager_fallback(
          input_dataset, key_func_other_arguments, init_func_other_arguments,
          reduce_func_other_arguments, finalize_func_other_arguments,
          key_func=key_func, init_func=init_func, reduce_func=reduce_func,
          finalize_func=finalize_func, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def group_by_reducer_dataset_eager_fallback(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function group_by_reducer_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'group_by_reducer_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'group_by_reducer_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx)
  _attr_Tinit_func_other_arguments, init_func_other_arguments = _execute.convert_to_mixed_eager_tensors(init_func_other_arguments, _ctx)
  _attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx)
  _attr_Tfinalize_func_other_arguments, finalize_func_other_arguments = _execute.convert_to_mixed_eager_tensors(finalize_func_other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(init_func_other_arguments) + list(reduce_func_other_arguments) + list(finalize_func_other_arguments)
  _attrs = ("key_func", key_func, "init_func", init_func, "reduce_func",
  reduce_func, "finalize_func", finalize_func, "Tkey_func_other_arguments",
  _attr_Tkey_func_other_arguments, "Tinit_func_other_arguments",
  _attr_Tinit_func_other_arguments, "Treduce_func_other_arguments",
  _attr_Treduce_func_other_arguments, "Tfinalize_func_other_arguments",
  _attr_Tfinalize_func_other_arguments, "output_types", output_types,
  "output_shapes", output_shapes)
  _result = _execute.execute(b"GroupByReducerDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "GroupByReducerDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None):
  r"""Creates a dataset that computes a windowed group-by on `input_dataset`.

  // TODO(mrry): Support non-int64 keys.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    key_func_other_arguments: A list of `Tensor` objects.
    reduce_func_other_arguments: A list of `Tensor` objects.
    window_size_func_other_arguments: A list of `Tensor` objects.
    key_func: A function decorated with @Defun.
      A function mapping an element of `input_dataset`, concatenated
      with `key_func_other_arguments` to a scalar value of type DT_INT64.
    reduce_func: A function decorated with @Defun.
    window_size_func: A function decorated with @Defun.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'group_by_window_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'group_by_window_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "GroupByWindowDataset", input_dataset=input_dataset,
        key_func_other_arguments=key_func_other_arguments,
        reduce_func_other_arguments=reduce_func_other_arguments,
        window_size_func_other_arguments=window_size_func_other_arguments,
        key_func=key_func, reduce_func=reduce_func,
        window_size_func=window_size_func, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("key_func", _op.get_attr("key_func"), "reduce_func",
              _op.get_attr("reduce_func"), "window_size_func",
              _op.get_attr("window_size_func"), "Tkey_func_other_arguments",
              _op.get_attr("Tkey_func_other_arguments"),
              "Treduce_func_other_arguments",
              _op.get_attr("Treduce_func_other_arguments"),
              "Twindow_size_func_other_arguments",
              _op.get_attr("Twindow_size_func_other_arguments"),
              "output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "GroupByWindowDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "GroupByWindowDataset", name, _ctx._post_execution_callbacks,
        input_dataset, key_func_other_arguments, reduce_func_other_arguments,
        window_size_func_other_arguments, "key_func", key_func, "reduce_func",
        reduce_func, "window_size_func", window_size_func, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return group_by_window_dataset_eager_fallback(
          input_dataset, key_func_other_arguments,
          reduce_func_other_arguments, window_size_func_other_arguments,
          key_func=key_func, reduce_func=reduce_func,
          window_size_func=window_size_func, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def group_by_window_dataset_eager_fallback(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function group_by_window_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'group_by_window_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'group_by_window_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx)
  _attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx)
  _attr_Twindow_size_func_other_arguments, window_size_func_other_arguments = _execute.convert_to_mixed_eager_tensors(window_size_func_other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(reduce_func_other_arguments) + list(window_size_func_other_arguments)
  _attrs = ("key_func", key_func, "reduce_func", reduce_func,
  "window_size_func", window_size_func, "Tkey_func_other_arguments",
  _attr_Tkey_func_other_arguments, "Treduce_func_other_arguments",
  _attr_Treduce_func_other_arguments, "Twindow_size_func_other_arguments",
  _attr_Twindow_size_func_other_arguments, "output_types", output_types,
  "output_shapes", output_shapes)
  _result = _execute.execute(b"GroupByWindowDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "GroupByWindowDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  Unlike MapDataset, the `f` in InterleaveDataset is expected to return
  a Dataset variant, and InterleaveDataset will flatten successive
  results into a single Dataset. Unlike FlatMapDataset,
  InterleaveDataset will interleave sequences of up to `block_length`
  consecutive elements from `cycle_length` input elements.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    cycle_length: A `Tensor` of type `int64`.
    block_length: A `Tensor` of type `int64`.
    f: A function decorated with @Defun.
      A function mapping elements of `input_dataset`, concatenated with
      `other_arguments`, to a Dataset variant that contains elements matching
      `output_types` and `output_shapes`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'interleave_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'interleave_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "InterleaveDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, cycle_length=cycle_length,
        block_length=block_length, f=f, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "InterleaveDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "InterleaveDataset", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, cycle_length, block_length, "f", f,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return interleave_dataset_eager_fallback(
          input_dataset, other_arguments, cycle_length, block_length, f=f,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def interleave_dataset_eager_fallback(input_dataset, other_arguments, cycle_length, block_length, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function interleave_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'interleave_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'interleave_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  cycle_length = _ops.convert_to_tensor(cycle_length, _dtypes.int64)
  block_length = _ops.convert_to_tensor(block_length, _dtypes.int64)
  _inputs_flat = [input_dataset] + list(other_arguments) + [cycle_length, block_length]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"InterleaveDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "InterleaveDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator(shared_name, container, output_types, output_shapes, name=None):
  r"""A container for an iterator resource.

  Args:
    shared_name: A `string`.
    container: A `string`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    shared_name = _execute.make_str(shared_name, "shared_name")
    container = _execute.make_str(container, "container")
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "Iterator", shared_name=shared_name, container=container,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("shared_name", _op.get_attr("shared_name"), "container",
              _op.get_attr("container"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "Iterator", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "Iterator",
        name, _ctx._post_execution_callbacks, "shared_name", shared_name,
        "container", container, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_eager_fallback(
          shared_name=shared_name, container=container,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_eager_fallback(shared_name, container, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator
  """
  _ctx = ctx if ctx else _context.context()
  shared_name = _execute.make_str(shared_name, "shared_name")
  container = _execute.make_str(container, "container")
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _inputs_flat = []
  _attrs = ("shared_name", shared_name, "container", container,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"Iterator", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "Iterator", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator_from_string_handle(string_handle, output_types=[], output_shapes=[], name=None):
  r"""Converts the given string representing a handle to an iterator to a resource.

  Args:
    string_handle: A `Tensor` of type `string`.
      A string representation of the given handle.
    output_types: An optional list of `tf.DTypes`. Defaults to `[]`.
      If specified, defines the type of each tuple component in an
      element produced by the resulting iterator.
    output_shapes: An optional list of shapes (each a `tf.TensorShape` or list of `ints`). Defaults to `[]`.
      If specified, defines the shape of each tuple component in an
      element produced by the resulting iterator.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if output_types is None:
      output_types = []
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_from_string_handle' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if output_shapes is None:
      output_shapes = []
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_from_string_handle' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorFromStringHandle", string_handle=string_handle,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorFromStringHandle", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorFromStringHandle", name, _ctx._post_execution_callbacks,
        string_handle, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_from_string_handle_eager_fallback(
          string_handle, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_from_string_handle_eager_fallback(string_handle, output_types=[], output_shapes=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_from_string_handle
  """
  _ctx = ctx if ctx else _context.context()
  if output_types is None:
    output_types = []
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_from_string_handle' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if output_shapes is None:
    output_shapes = []
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_from_string_handle' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  string_handle = _ops.convert_to_tensor(string_handle, _dtypes.string)
  _inputs_flat = [string_handle]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorFromStringHandle", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorFromStringHandle", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator_from_string_handle_v2(string_handle, output_types=[], output_shapes=[], name=None):
  r"""TODO: add doc.

  Args:
    string_handle: A `Tensor` of type `string`.
    output_types: An optional list of `tf.DTypes`. Defaults to `[]`.
    output_shapes: An optional list of shapes (each a `tf.TensorShape` or list of `ints`). Defaults to `[]`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if output_types is None:
      output_types = []
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_from_string_handle_v2' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if output_shapes is None:
      output_shapes = []
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_from_string_handle_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorFromStringHandleV2", string_handle=string_handle,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorFromStringHandleV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorFromStringHandleV2", name, _ctx._post_execution_callbacks,
        string_handle, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_from_string_handle_v2_eager_fallback(
          string_handle, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_from_string_handle_v2_eager_fallback(string_handle, output_types=[], output_shapes=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_from_string_handle_v2
  """
  _ctx = ctx if ctx else _context.context()
  if output_types is None:
    output_types = []
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_from_string_handle_v2' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if output_shapes is None:
    output_shapes = []
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_from_string_handle_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  string_handle = _ops.convert_to_tensor(string_handle, _dtypes.string)
  _inputs_flat = [string_handle]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorFromStringHandleV2", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorFromStringHandleV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator_get_next(iterator, output_types, output_shapes, name=None):
  r"""Gets the next output from the given iterator .

  Args:
    iterator: A `Tensor` of type `resource`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_get_next' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_get_next' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorGetNext", iterator=iterator, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    if not _result:
      return _op
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorGetNext", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorGetNext", name, _ctx._post_execution_callbacks, iterator,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_get_next_eager_fallback(
          iterator, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_get_next_eager_fallback(iterator, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_get_next
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_get_next' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_get_next' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
  _inputs_flat = [iterator]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorGetNext", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorGetNext", _inputs_flat, _attrs, _result, name)
  return _result


def iterator_get_next_as_optional(iterator, output_types, output_shapes, name=None):
  r"""Gets the next output from the given iterator as an Optional variant.

  Args:
    iterator: A `Tensor` of type `resource`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_get_next_as_optional' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_get_next_as_optional' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorGetNextAsOptional", iterator=iterator,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorGetNextAsOptional", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorGetNextAsOptional", name, _ctx._post_execution_callbacks,
        iterator, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_get_next_as_optional_eager_fallback(
          iterator, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_get_next_as_optional_eager_fallback(iterator, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_get_next_as_optional
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_get_next_as_optional' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_get_next_as_optional' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
  _inputs_flat = [iterator]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorGetNextAsOptional", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorGetNextAsOptional", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator_get_next_sync(iterator, output_types, output_shapes, name=None):
  r"""Gets the next output from the given iterator.

  This operation is a synchronous version IteratorGetNext. It should only be used
  in situations where the iterator does not block the calling thread, or where
  the calling thread is not a member of the thread pool used to execute parallel
  operations (e.g. in eager mode).

  Args:
    iterator: A `Tensor` of type `resource`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_get_next_sync' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_get_next_sync' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorGetNextSync", iterator=iterator, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    if not _result:
      return _op
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorGetNextSync", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorGetNextSync", name, _ctx._post_execution_callbacks, iterator,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_get_next_sync_eager_fallback(
          iterator, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_get_next_sync_eager_fallback(iterator, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_get_next_sync
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_get_next_sync' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_get_next_sync' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
  _inputs_flat = [iterator]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorGetNextSync", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorGetNextSync", _inputs_flat, _attrs, _result, name)
  return _result


def iterator_to_string_handle(resource_handle, name=None):
  r"""Converts the given `resource_handle` representing an iterator to a string.

  Args:
    resource_handle: A `Tensor` of type `resource`.
      A handle to an iterator resource.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorToStringHandle", resource_handle=resource_handle, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "IteratorToStringHandle", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "IteratorToStringHandle", name, _ctx._post_execution_callbacks,
        resource_handle)
      return _result
    except _core._FallbackException:
      return iterator_to_string_handle_eager_fallback(
          resource_handle, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_to_string_handle_eager_fallback(resource_handle, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_to_string_handle
  """
  _ctx = ctx if ctx else _context.context()
  resource_handle = _ops.convert_to_tensor(resource_handle, _dtypes.resource)
  _inputs_flat = [resource_handle]
  _attrs = None
  _result = _execute.execute(b"IteratorToStringHandle", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "IteratorToStringHandle", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def iterator_v2(shared_name, container, output_types, output_shapes, name=None):
  r"""TODO: add doc.

  Args:
    shared_name: A `string`.
    container: A `string`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    shared_name = _execute.make_str(shared_name, "shared_name")
    container = _execute.make_str(container, "container")
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'iterator_v2' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'iterator_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "IteratorV2", shared_name=shared_name, container=container,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("shared_name", _op.get_attr("shared_name"), "container",
              _op.get_attr("container"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "IteratorV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "IteratorV2",
        name, _ctx._post_execution_callbacks, "shared_name", shared_name,
        "container", container, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return iterator_v2_eager_fallback(
          shared_name=shared_name, container=container,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def iterator_v2_eager_fallback(shared_name, container, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function iterator_v2
  """
  _ctx = ctx if ctx else _context.context()
  shared_name = _execute.make_str(shared_name, "shared_name")
  container = _execute.make_str(container, "container")
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'iterator_v2' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'iterator_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _inputs_flat = []
  _attrs = ("shared_name", shared_name, "container", container,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"IteratorV2", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "IteratorV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def latency_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None):
  r"""Records the latency of producing `input_dataset` elements in a StatsAggregator.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    tag: A `Tensor` of type `string`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'latency_stats_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'latency_stats_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "LatencyStatsDataset", input_dataset=input_dataset, tag=tag,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "LatencyStatsDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "LatencyStatsDataset", name, _ctx._post_execution_callbacks,
        input_dataset, tag, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return latency_stats_dataset_eager_fallback(
          input_dataset, tag, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def latency_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function latency_stats_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'latency_stats_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'latency_stats_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  tag = _ops.convert_to_tensor(tag, _dtypes.string)
  _inputs_flat = [input_dataset, tag]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"LatencyStatsDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "LatencyStatsDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def make_iterator(dataset, iterator, name=None):
  r"""Makes a new iterator from the given `dataset` and stores it in `iterator`.

  This operation may be executed multiple times. Each execution will reset the
  iterator in `iterator` to the first element of `dataset`.

  Args:
    dataset: A `Tensor` of type `variant`.
    iterator: A `Tensor` of type `resource`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "MakeIterator", dataset=dataset, iterator=iterator, name=name)
    return _op
    _result = None
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "MakeIterator",
        name, _ctx._post_execution_callbacks, dataset, iterator)
      return _result
    except _core._FallbackException:
      return make_iterator_eager_fallback(
          dataset, iterator, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def make_iterator_eager_fallback(dataset, iterator, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function make_iterator
  """
  _ctx = ctx if ctx else _context.context()
  dataset = _ops.convert_to_tensor(dataset, _dtypes.variant)
  iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
  _inputs_flat = [dataset, iterator]
  _attrs = None
  _result = _execute.execute(b"MakeIterator", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_batches, drop_remainder, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that fuses mapping with batching.

  Creates a dataset that applies `f` to the outputs of `input_dataset` and then
  batches `batch_size` of them.

  Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
  to `batch_size * num_parallel_batches` copies of `f` in parallel.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when building a closure
      for `f`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch. It determines the number of concurrent invocations of `f` that process
      elements from `input_dataset` in parallel.
    num_parallel_batches: A `Tensor` of type `int64`.
      A scalar representing the number of batches to create in parallel. Processing
      multiple batches in parallel benefits workloads prone to stragglers.
    drop_remainder: A `Tensor` of type `bool`.
      A scalar representing whether the last batch should be dropped in case its size
      is smaller than desired.
    f: A function decorated with @Defun.
      A function to apply to the outputs of `input_dataset`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'map_and_batch_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'map_and_batch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MapAndBatchDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, batch_size=batch_size,
        num_parallel_batches=num_parallel_batches,
        drop_remainder=drop_remainder, f=f, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "MapAndBatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MapAndBatchDataset", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, batch_size, num_parallel_batches,
        drop_remainder, "f", f, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return map_and_batch_dataset_eager_fallback(
          input_dataset, other_arguments, batch_size, num_parallel_batches,
          drop_remainder, f=f, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def map_and_batch_dataset_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_batches, drop_remainder, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function map_and_batch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'map_and_batch_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'map_and_batch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  num_parallel_batches = _ops.convert_to_tensor(num_parallel_batches, _dtypes.int64)
  drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
  _inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_batches, drop_remainder]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"MapAndBatchDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "MapAndBatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def map_and_batch_dataset_v2(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that fuses mapping with batching.

  Creates a dataset that applies `f` to the outputs of `input_dataset` and then
  batches `batch_size` of them.

  Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
  to `batch_size * num_parallel_batches` copies of `f` in parallel.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    other_arguments: A list of `Tensor` objects.
      A list of tensors, typically values that were captured when building a closure
      for `f`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch. It determines the number of concurrent invocations of `f` that process
      elements from `input_dataset` in parallel.
    num_parallel_calls: A `Tensor` of type `int64`.
      A scalar representing the maximum number of parallel invocations of the `map_fn`
      function. Applying the `map_fn` on consecutive input elements in parallel has
      the potential to improve input pipeline throughput.
    drop_remainder: A `Tensor` of type `bool`.
      A scalar representing whether the last batch should be dropped in case its size
      is smaller than desired.
    f: A function decorated with @Defun.
      A function to apply to the outputs of `input_dataset`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'map_and_batch_dataset_v2' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'map_and_batch_dataset_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MapAndBatchDatasetV2", input_dataset=input_dataset,
        other_arguments=other_arguments, batch_size=batch_size,
        num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder,
        f=f, output_types=output_types, output_shapes=output_shapes,
        name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "MapAndBatchDatasetV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MapAndBatchDatasetV2", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, batch_size, num_parallel_calls,
        drop_remainder, "f", f, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return map_and_batch_dataset_v2_eager_fallback(
          input_dataset, other_arguments, batch_size, num_parallel_calls,
          drop_remainder, f=f, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def map_and_batch_dataset_v2_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function map_and_batch_dataset_v2
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'map_and_batch_dataset_v2' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'map_and_batch_dataset_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
  drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
  _inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_calls, drop_remainder]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"MapAndBatchDatasetV2", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "MapAndBatchDatasetV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def map_dataset(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    f: A function decorated with @Defun.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    use_inter_op_parallelism: An optional `bool`. Defaults to `True`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'map_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'map_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    if use_inter_op_parallelism is None:
      use_inter_op_parallelism = True
    use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
    _, _, _op = _op_def_lib._apply_op_helper(
        "MapDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, f=f, output_types=output_types,
        output_shapes=output_shapes,
        use_inter_op_parallelism=use_inter_op_parallelism, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "use_inter_op_parallelism",
              _op.get_attr("use_inter_op_parallelism"))
    _execute.record_gradient(
      "MapDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "MapDataset",
        name, _ctx._post_execution_callbacks, input_dataset, other_arguments,
        "f", f, "output_types", output_types, "output_shapes", output_shapes,
        "use_inter_op_parallelism", use_inter_op_parallelism)
      return _result
    except _core._FallbackException:
      return map_dataset_eager_fallback(
          input_dataset, other_arguments, f=f, output_types=output_types,
          output_shapes=output_shapes,
          use_inter_op_parallelism=use_inter_op_parallelism, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def map_dataset_eager_fallback(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function map_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'map_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'map_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  if use_inter_op_parallelism is None:
    use_inter_op_parallelism = True
  use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(other_arguments)
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes, "use_inter_op_parallelism",
  use_inter_op_parallelism)
  _result = _execute.execute(b"MapDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "MapDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def map_defun(arguments, output_types, output_shapes, f, name=None):
  r"""  Maps a function on the list of tensors unpacked from inputs on dimension 0.

  Args:
    arguments: A list of `Tensor` objects.
          A list of tensors whose types are Targuments, corresponding to the inputs the
          function should be mapped over.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
      A list of types.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
      A list of shapes.
    f: A function decorated with @Defun.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'map_defun' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'map_defun' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MapDefun", arguments=arguments, output_types=output_types,
        output_shapes=output_shapes, f=f, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Targuments", _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "f", _op.get_attr("f"))
    _execute.record_gradient(
      "MapDefun", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "MapDefun",
        name, _ctx._post_execution_callbacks, arguments, "output_types",
        output_types, "output_shapes", output_shapes, "f", f)
      return _result
    except _core._FallbackException:
      return map_defun_eager_fallback(
          arguments, output_types=output_types, output_shapes=output_shapes,
          f=f, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def map_defun_eager_fallback(arguments, output_types, output_shapes, f, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function map_defun
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'map_defun' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'map_defun' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, arguments = _execute.convert_to_mixed_eager_tensors(arguments, _ctx)
  _inputs_flat = list(arguments)
  _attrs = ("Targuments", _attr_Targuments, "output_types", output_types,
  "output_shapes", output_shapes, "f", f)
  _result = _execute.execute(b"MapDefun", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "MapDefun", _inputs_flat, _attrs, _result, name)
  return _result


def model_dataset(input_dataset, output_types, output_shapes, name=None):
  r"""Identity transformation that models performance.

  Identity transformation that models performance.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'model_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'model_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ModelDataset", input_dataset=input_dataset,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ModelDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "ModelDataset",
        name, _ctx._post_execution_callbacks, input_dataset, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return model_dataset_eager_fallback(
          input_dataset, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def model_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function model_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'model_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'model_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ModelDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ModelDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def multi_device_iterator(devices, shared_name, container, output_types, output_shapes, name=None):
  r"""Creates a MultiDeviceIterator resource.

  Args:
    devices: A list of `strings` that has length `>= 1`.
      A list of devices the iterator works across.
    shared_name: A `string`.
      If non-empty, this resource will be shared under the given name
      across multiple sessions.
    container: A `string`.
      If non-empty, this resource is placed in the given container.
      Otherwise, a default container is used.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
      The type list for the return values.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
      The list of shapes being produced.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(devices, (list, tuple)):
      raise TypeError(
          "Expected list for 'devices' argument to "
          "'multi_device_iterator' Op, not %r." % devices)
    devices = [_execute.make_str(_s, "devices") for _s in devices]
    shared_name = _execute.make_str(shared_name, "shared_name")
    container = _execute.make_str(container, "container")
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'multi_device_iterator' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'multi_device_iterator' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MultiDeviceIterator", devices=devices, shared_name=shared_name,
        container=container, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("devices", _op.get_attr("devices"), "shared_name",
              _op.get_attr("shared_name"), "container",
              _op.get_attr("container"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "MultiDeviceIterator", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MultiDeviceIterator", name, _ctx._post_execution_callbacks,
        "devices", devices, "shared_name", shared_name, "container",
        container, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return multi_device_iterator_eager_fallback(
          devices=devices, shared_name=shared_name, container=container,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def multi_device_iterator_eager_fallback(devices, shared_name, container, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function multi_device_iterator
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(devices, (list, tuple)):
    raise TypeError(
        "Expected list for 'devices' argument to "
        "'multi_device_iterator' Op, not %r." % devices)
  devices = [_execute.make_str(_s, "devices") for _s in devices]
  shared_name = _execute.make_str(shared_name, "shared_name")
  container = _execute.make_str(container, "container")
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'multi_device_iterator' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'multi_device_iterator' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _inputs_flat = []
  _attrs = ("devices", devices, "shared_name", shared_name, "container",
  container, "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"MultiDeviceIterator", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "MultiDeviceIterator", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def multi_device_iterator_from_string_handle(string_handle, output_types=[], output_shapes=[], name=None):
  r"""Generates a MultiDeviceIterator resource from its provided string handle.

  Args:
    string_handle: A `Tensor` of type `string`.
      String representing the resource.
    output_types: An optional list of `tf.DTypes`. Defaults to `[]`.
      The type list for the return values.
    output_shapes: An optional list of shapes (each a `tf.TensorShape` or list of `ints`). Defaults to `[]`.
      The list of shapes being produced.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if output_types is None:
      output_types = []
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'multi_device_iterator_from_string_handle' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if output_shapes is None:
      output_shapes = []
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'multi_device_iterator_from_string_handle' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MultiDeviceIteratorFromStringHandle", string_handle=string_handle,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "MultiDeviceIteratorFromStringHandle", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MultiDeviceIteratorFromStringHandle", name,
        _ctx._post_execution_callbacks, string_handle, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return multi_device_iterator_from_string_handle_eager_fallback(
          string_handle, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def multi_device_iterator_from_string_handle_eager_fallback(string_handle, output_types=[], output_shapes=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function multi_device_iterator_from_string_handle
  """
  _ctx = ctx if ctx else _context.context()
  if output_types is None:
    output_types = []
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'multi_device_iterator_from_string_handle' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if output_shapes is None:
    output_shapes = []
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'multi_device_iterator_from_string_handle' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  string_handle = _ops.convert_to_tensor(string_handle, _dtypes.string)
  _inputs_flat = [string_handle]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"MultiDeviceIteratorFromStringHandle", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "MultiDeviceIteratorFromStringHandle", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def multi_device_iterator_get_next_from_shard(multi_device_iterator, shard_num, incarnation_id, output_types, output_shapes, name=None):
  r"""Gets next element for the provided shard number.

  Args:
    multi_device_iterator: A `Tensor` of type `resource`.
      A MultiDeviceIterator resource.
    shard_num: A `Tensor` of type `int32`.
      Integer representing which shard to fetch data for.
    incarnation_id: A `Tensor` of type `int64`.
      Which incarnation of the MultiDeviceIterator is running.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
      The type list for the return values.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
      The list of shapes being produced.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'multi_device_iterator_get_next_from_shard' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'multi_device_iterator_get_next_from_shard' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "MultiDeviceIteratorGetNextFromShard",
        multi_device_iterator=multi_device_iterator, shard_num=shard_num,
        incarnation_id=incarnation_id, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    if not _result:
      return _op
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "MultiDeviceIteratorGetNextFromShard", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MultiDeviceIteratorGetNextFromShard", name,
        _ctx._post_execution_callbacks, multi_device_iterator, shard_num,
        incarnation_id, "output_types", output_types, "output_shapes",
        output_shapes)
      return _result
    except _core._FallbackException:
      return multi_device_iterator_get_next_from_shard_eager_fallback(
          multi_device_iterator, shard_num, incarnation_id,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def multi_device_iterator_get_next_from_shard_eager_fallback(multi_device_iterator, shard_num, incarnation_id, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function multi_device_iterator_get_next_from_shard
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'multi_device_iterator_get_next_from_shard' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'multi_device_iterator_get_next_from_shard' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  multi_device_iterator = _ops.convert_to_tensor(multi_device_iterator, _dtypes.resource)
  shard_num = _ops.convert_to_tensor(shard_num, _dtypes.int32)
  incarnation_id = _ops.convert_to_tensor(incarnation_id, _dtypes.int64)
  _inputs_flat = [multi_device_iterator, shard_num, incarnation_id]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"MultiDeviceIteratorGetNextFromShard",
                             len(output_types), inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "MultiDeviceIteratorGetNextFromShard", _inputs_flat, _attrs, _result, name)
  return _result


def multi_device_iterator_init(dataset, multi_device_iterator, max_buffer_size, name=None):
  r"""Initializes the multi device iterator with the given dataset.

  Args:
    dataset: A `Tensor` of type `variant`. Dataset to be iterated upon.
    multi_device_iterator: A `Tensor` of type `resource`.
      A MultiDeviceIteratorResource.
    max_buffer_size: A `Tensor` of type `int64`.
      The maximum size of the host side per device buffer to keep.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `int64`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "MultiDeviceIteratorInit", dataset=dataset,
        multi_device_iterator=multi_device_iterator,
        max_buffer_size=max_buffer_size, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "MultiDeviceIteratorInit", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MultiDeviceIteratorInit", name, _ctx._post_execution_callbacks,
        dataset, multi_device_iterator, max_buffer_size)
      return _result
    except _core._FallbackException:
      return multi_device_iterator_init_eager_fallback(
          dataset, multi_device_iterator, max_buffer_size, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def multi_device_iterator_init_eager_fallback(dataset, multi_device_iterator, max_buffer_size, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function multi_device_iterator_init
  """
  _ctx = ctx if ctx else _context.context()
  dataset = _ops.convert_to_tensor(dataset, _dtypes.variant)
  multi_device_iterator = _ops.convert_to_tensor(multi_device_iterator, _dtypes.resource)
  max_buffer_size = _ops.convert_to_tensor(max_buffer_size, _dtypes.int64)
  _inputs_flat = [dataset, multi_device_iterator, max_buffer_size]
  _attrs = None
  _result = _execute.execute(b"MultiDeviceIteratorInit", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "MultiDeviceIteratorInit", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def multi_device_iterator_to_string_handle(multi_device_iterator, name=None):
  r"""Produces a string handle for the given MultiDeviceIterator.

  Args:
    multi_device_iterator: A `Tensor` of type `resource`.
      A MultiDeviceIterator resource.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "MultiDeviceIteratorToStringHandle",
        multi_device_iterator=multi_device_iterator, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "MultiDeviceIteratorToStringHandle", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "MultiDeviceIteratorToStringHandle", name,
        _ctx._post_execution_callbacks, multi_device_iterator)
      return _result
    except _core._FallbackException:
      return multi_device_iterator_to_string_handle_eager_fallback(
          multi_device_iterator, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def multi_device_iterator_to_string_handle_eager_fallback(multi_device_iterator, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function multi_device_iterator_to_string_handle
  """
  _ctx = ctx if ctx else _context.context()
  multi_device_iterator = _ops.convert_to_tensor(multi_device_iterator, _dtypes.resource)
  _inputs_flat = [multi_device_iterator]
  _attrs = None
  _result = _execute.execute(b"MultiDeviceIteratorToStringHandle", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "MultiDeviceIteratorToStringHandle", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def one_shot_iterator(dataset_factory, output_types, output_shapes, container="", shared_name="", name=None):
  r"""Makes a "one-shot" iterator that can be iterated only once.

  A one-shot iterator bundles the logic for defining the dataset and
  the state of the iterator in a single op, which allows simple input
  pipelines to be defined without an additional initialization
  ("MakeIterator") step.

  One-shot iterators have the following limitations:

  * They do not support parameterization: all logic for creating the underlying
    dataset must be bundled in the `dataset_factory` function.
  * They are not resettable. Once a one-shot iterator reaches the end of its
    underlying dataset, subsequent "IteratorGetNext" operations on that
    iterator will always produce an `OutOfRange` error.

  For greater flexibility, use "Iterator" and "MakeIterator" to define
  an iterator using an arbitrary subgraph, which may capture tensors
  (including fed values) as parameters, and which may be reset multiple
  times by rerunning "MakeIterator".

  Args:
    dataset_factory: A function decorated with @Defun.
      A function of type `() -> DT_VARIANT`, where the returned
      DT_VARIANT is a dataset.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    container: An optional `string`. Defaults to `""`.
    shared_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'one_shot_iterator' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'one_shot_iterator' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    if container is None:
      container = ""
    container = _execute.make_str(container, "container")
    if shared_name is None:
      shared_name = ""
    shared_name = _execute.make_str(shared_name, "shared_name")
    _, _, _op = _op_def_lib._apply_op_helper(
        "OneShotIterator", dataset_factory=dataset_factory,
        output_types=output_types, output_shapes=output_shapes,
        container=container, shared_name=shared_name, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("dataset_factory", _op.get_attr("dataset_factory"),
              "output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "container",
              _op.get_attr("container"), "shared_name",
              _op.get_attr("shared_name"))
    _execute.record_gradient(
      "OneShotIterator", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "OneShotIterator", name, _ctx._post_execution_callbacks,
        "dataset_factory", dataset_factory, "output_types", output_types,
        "output_shapes", output_shapes, "container", container, "shared_name",
        shared_name)
      return _result
    except _core._FallbackException:
      return one_shot_iterator_eager_fallback(
          dataset_factory=dataset_factory, output_types=output_types,
          output_shapes=output_shapes, container=container,
          shared_name=shared_name, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def one_shot_iterator_eager_fallback(dataset_factory, output_types, output_shapes, container="", shared_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function one_shot_iterator
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'one_shot_iterator' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'one_shot_iterator' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  if container is None:
    container = ""
  container = _execute.make_str(container, "container")
  if shared_name is None:
    shared_name = ""
  shared_name = _execute.make_str(shared_name, "shared_name")
  _inputs_flat = []
  _attrs = ("dataset_factory", dataset_factory, "output_types", output_types,
  "output_shapes", output_shapes, "container", container, "shared_name",
  shared_name)
  _result = _execute.execute(b"OneShotIterator", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OneShotIterator", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def optimize_dataset(input_dataset, optimizations, output_types, output_shapes, name=None):
  r"""Creates a dataset by applying optimizations to `input_dataset`.

  Creates a dataset by applying optimizations to `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    optimizations: A `Tensor` of type `string`.
      A `tf.string` vector `tf.Tensor` identifying optimizations to use.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'optimize_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'optimize_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "OptimizeDataset", input_dataset=input_dataset,
        optimizations=optimizations, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "OptimizeDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "OptimizeDataset", name, _ctx._post_execution_callbacks,
        input_dataset, optimizations, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return optimize_dataset_eager_fallback(
          input_dataset, optimizations, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def optimize_dataset_eager_fallback(input_dataset, optimizations, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function optimize_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'optimize_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'optimize_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  optimizations = _ops.convert_to_tensor(optimizations, _dtypes.string)
  _inputs_flat = [input_dataset, optimizations]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"OptimizeDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OptimizeDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def optional_from_value(components, name=None):
  r"""Constructs an Optional variant from a tuple of tensors.

  Args:
    components: A list of `Tensor` objects.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "OptionalFromValue", components=components, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"))
    _execute.record_gradient(
      "OptionalFromValue", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "OptionalFromValue", name, _ctx._post_execution_callbacks, components)
      return _result
    except _core._FallbackException:
      return optional_from_value_eager_fallback(
          components, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def optional_from_value_eager_fallback(components, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function optional_from_value
  """
  _ctx = ctx if ctx else _context.context()
  _attr_Toutput_types, components = _execute.convert_to_mixed_eager_tensors(components, _ctx)
  _inputs_flat = list(components)
  _attrs = ("Toutput_types", _attr_Toutput_types)
  _result = _execute.execute(b"OptionalFromValue", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OptionalFromValue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def optional_get_value(optional, output_types, output_shapes, name=None):
  r"""Returns the value stored in an Optional variant or raises an error if none exists.

  Args:
    optional: A `Tensor` of type `variant`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'optional_get_value' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'optional_get_value' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "OptionalGetValue", optional=optional, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "OptionalGetValue", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "OptionalGetValue", name, _ctx._post_execution_callbacks, optional,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return optional_get_value_eager_fallback(
          optional, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def optional_get_value_eager_fallback(optional, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function optional_get_value
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'optional_get_value' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'optional_get_value' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  optional = _ops.convert_to_tensor(optional, _dtypes.variant)
  _inputs_flat = [optional]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"OptionalGetValue", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "OptionalGetValue", _inputs_flat, _attrs, _result, name)
  return _result


def optional_has_value(optional, name=None):
  r"""Returns true if and only if the given Optional variant has a value.

  Args:
    optional: A `Tensor` of type `variant`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `bool`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "OptionalHasValue", optional=optional, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "OptionalHasValue", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "OptionalHasValue", name, _ctx._post_execution_callbacks, optional)
      return _result
    except _core._FallbackException:
      return optional_has_value_eager_fallback(
          optional, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def optional_has_value_eager_fallback(optional, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function optional_has_value
  """
  _ctx = ctx if ctx else _context.context()
  optional = _ops.convert_to_tensor(optional, _dtypes.variant)
  _inputs_flat = [optional]
  _attrs = None
  _result = _execute.execute(b"OptionalHasValue", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OptionalHasValue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def optional_none(name=None):
  r"""Creates an Optional variant with no value.

  Args:
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "OptionalNone", name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "OptionalNone", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "OptionalNone",
        name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      return optional_none_eager_fallback(
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def optional_none_eager_fallback(name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function optional_none
  """
  _ctx = ctx if ctx else _context.context()
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"OptionalNone", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OptionalNone", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def padded_batch_dataset(input_dataset, batch_size, padded_shapes, padding_values, output_shapes, name=None):
  r"""Creates a dataset that batches and pads `batch_size` elements from the input.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch.
    padded_shapes: A list of at least 1 `Tensor` objects with type `int64`.
      A list of int64 tensors representing the desired padded shapes
      of the corresponding output components. These shapes may be partially
      specified, using `-1` to indicate that a particular dimension should be
      padded to the maximum size of all batch elements.
    padding_values: A list of `Tensor` objects.
      A list of scalars containing the padding value to use for
      each of the outputs.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(padded_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'padded_shapes' argument to "
          "'padded_batch_dataset' Op, not %r." % padded_shapes)
    _attr_N = len(padded_shapes)
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'padded_batch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "PaddedBatchDataset", input_dataset=input_dataset,
        batch_size=batch_size, padded_shapes=padded_shapes,
        padding_values=padding_values, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"), "output_shapes",
              _op.get_attr("output_shapes"), "N", _op.get_attr("N"))
    _execute.record_gradient(
      "PaddedBatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "PaddedBatchDataset", name, _ctx._post_execution_callbacks,
        input_dataset, batch_size, padded_shapes, padding_values,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return padded_batch_dataset_eager_fallback(
          input_dataset, batch_size, padded_shapes, padding_values,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def padded_batch_dataset_eager_fallback(input_dataset, batch_size, padded_shapes, padding_values, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function padded_batch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(padded_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'padded_shapes' argument to "
        "'padded_batch_dataset' Op, not %r." % padded_shapes)
  _attr_N = len(padded_shapes)
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'padded_batch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Toutput_types, padding_values = _execute.convert_to_mixed_eager_tensors(padding_values, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  padded_shapes = _ops.convert_n_to_tensor(padded_shapes, _dtypes.int64)
  _inputs_flat = [input_dataset, batch_size] + list(padded_shapes) + list(padding_values)
  _attrs = ("Toutput_types", _attr_Toutput_types, "output_shapes",
  output_shapes, "N", _attr_N)
  _result = _execute.execute(b"PaddedBatchDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "PaddedBatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def padded_batch_dataset_v2(input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, output_shapes, name=None):
  r"""Creates a dataset that batches and pads `batch_size` elements from the input.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    batch_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a
      batch.
    padded_shapes: A list of at least 1 `Tensor` objects with type `int64`.
      A list of int64 tensors representing the desired padded shapes
      of the corresponding output components. These shapes may be partially
      specified, using `-1` to indicate that a particular dimension should be
      padded to the maximum size of all batch elements.
    padding_values: A list of `Tensor` objects.
      A list of scalars containing the padding value to use for
      each of the outputs.
    drop_remainder: A `Tensor` of type `bool`.
      A scalar representing whether the last batch should be dropped in case its size
      is smaller than desired.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(padded_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'padded_shapes' argument to "
          "'padded_batch_dataset_v2' Op, not %r." % padded_shapes)
    _attr_N = len(padded_shapes)
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'padded_batch_dataset_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "PaddedBatchDatasetV2", input_dataset=input_dataset,
        batch_size=batch_size, padded_shapes=padded_shapes,
        padding_values=padding_values, drop_remainder=drop_remainder,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"), "output_shapes",
              _op.get_attr("output_shapes"), "N", _op.get_attr("N"))
    _execute.record_gradient(
      "PaddedBatchDatasetV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "PaddedBatchDatasetV2", name, _ctx._post_execution_callbacks,
        input_dataset, batch_size, padded_shapes, padding_values,
        drop_remainder, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return padded_batch_dataset_v2_eager_fallback(
          input_dataset, batch_size, padded_shapes, padding_values,
          drop_remainder, output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def padded_batch_dataset_v2_eager_fallback(input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function padded_batch_dataset_v2
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(padded_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'padded_shapes' argument to "
        "'padded_batch_dataset_v2' Op, not %r." % padded_shapes)
  _attr_N = len(padded_shapes)
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'padded_batch_dataset_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Toutput_types, padding_values = _execute.convert_to_mixed_eager_tensors(padding_values, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  padded_shapes = _ops.convert_n_to_tensor(padded_shapes, _dtypes.int64)
  drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
  _inputs_flat = [input_dataset, batch_size] + list(padded_shapes) + list(padding_values) + [drop_remainder]
  _attrs = ("Toutput_types", _attr_Toutput_types, "output_shapes",
  output_shapes, "N", _attr_N)
  _result = _execute.execute(b"PaddedBatchDatasetV2", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "PaddedBatchDatasetV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  The resulting dataset is similar to the `InterleaveDataset`, with the exception
  that if retrieving the next value from a dataset would cause the requester to
  block, it will skip that input dataset. This dataset is especially useful
  when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it
  allows the training step to proceed so long as some data is available.

  !! WARNING !! This dataset is not deterministic!

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    cycle_length: A `Tensor` of type `int64`.
    block_length: A `Tensor` of type `int64`.
    sloppy: A `Tensor` of type `bool`.
    buffer_output_elements: A `Tensor` of type `int64`.
    prefetch_input_elements: A `Tensor` of type `int64`.
    f: A function decorated with @Defun.
      A function mapping elements of `input_dataset`, concatenated with
      `other_arguments`, to a Dataset variant that contains elements matching
      `output_types` and `output_shapes`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'parallel_interleave_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'parallel_interleave_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ParallelInterleaveDataset", input_dataset=input_dataset,
        other_arguments=other_arguments, cycle_length=cycle_length,
        block_length=block_length, sloppy=sloppy,
        buffer_output_elements=buffer_output_elements,
        prefetch_input_elements=prefetch_input_elements, f=f,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ParallelInterleaveDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ParallelInterleaveDataset", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, cycle_length, block_length, sloppy,
        buffer_output_elements, prefetch_input_elements, "f", f,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return parallel_interleave_dataset_eager_fallback(
          input_dataset, other_arguments, cycle_length, block_length, sloppy,
          buffer_output_elements, prefetch_input_elements, f=f,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def parallel_interleave_dataset_eager_fallback(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function parallel_interleave_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'parallel_interleave_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'parallel_interleave_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  cycle_length = _ops.convert_to_tensor(cycle_length, _dtypes.int64)
  block_length = _ops.convert_to_tensor(block_length, _dtypes.int64)
  sloppy = _ops.convert_to_tensor(sloppy, _dtypes.bool)
  buffer_output_elements = _ops.convert_to_tensor(buffer_output_elements, _dtypes.int64)
  prefetch_input_elements = _ops.convert_to_tensor(prefetch_input_elements, _dtypes.int64)
  _inputs_flat = [input_dataset] + list(other_arguments) + [cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ParallelInterleaveDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "ParallelInterleaveDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def parallel_interleave_dataset_v2(input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f, output_types, output_shapes, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    cycle_length: A `Tensor` of type `int64`.
    block_length: A `Tensor` of type `int64`.
    num_parallel_calls: A `Tensor` of type `int64`.
    f: A function decorated with @Defun.
      A function mapping elements of `input_dataset`, concatenated with
      `other_arguments`, to a Dataset variant that contains elements matching
      `output_types` and `output_shapes`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'parallel_interleave_dataset_v2' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'parallel_interleave_dataset_v2' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ParallelInterleaveDatasetV2", input_dataset=input_dataset,
        other_arguments=other_arguments, cycle_length=cycle_length,
        block_length=block_length, num_parallel_calls=num_parallel_calls, f=f,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ParallelInterleaveDatasetV2", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ParallelInterleaveDatasetV2", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, cycle_length, block_length,
        num_parallel_calls, "f", f, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return parallel_interleave_dataset_v2_eager_fallback(
          input_dataset, other_arguments, cycle_length, block_length,
          num_parallel_calls, f=f, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def parallel_interleave_dataset_v2_eager_fallback(input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function parallel_interleave_dataset_v2
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'parallel_interleave_dataset_v2' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'parallel_interleave_dataset_v2' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  cycle_length = _ops.convert_to_tensor(cycle_length, _dtypes.int64)
  block_length = _ops.convert_to_tensor(block_length, _dtypes.int64)
  num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
  _inputs_flat = [input_dataset] + list(other_arguments) + [cycle_length, block_length, num_parallel_calls]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ParallelInterleaveDatasetV2", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "ParallelInterleaveDatasetV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def parallel_map_dataset(input_dataset, other_arguments, num_parallel_calls, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None):
  r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.

  Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
  to `num_parallel_calls` copies of `f` in parallel.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    other_arguments: A list of `Tensor` objects.
    num_parallel_calls: A `Tensor` of type `int32`.
      The number of concurrent invocations of `f` that process
      elements from `input_dataset` in parallel.
    f: A function decorated with @Defun.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    use_inter_op_parallelism: An optional `bool`. Defaults to `True`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'parallel_map_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'parallel_map_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    if use_inter_op_parallelism is None:
      use_inter_op_parallelism = True
    use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
    _, _, _op = _op_def_lib._apply_op_helper(
        "ParallelMapDataset", input_dataset=input_dataset,
        other_arguments=other_arguments,
        num_parallel_calls=num_parallel_calls, f=f, output_types=output_types,
        output_shapes=output_shapes,
        use_inter_op_parallelism=use_inter_op_parallelism, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Targuments",
              _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "use_inter_op_parallelism",
              _op.get_attr("use_inter_op_parallelism"))
    _execute.record_gradient(
      "ParallelMapDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ParallelMapDataset", name, _ctx._post_execution_callbacks,
        input_dataset, other_arguments, num_parallel_calls, "f", f,
        "output_types", output_types, "output_shapes", output_shapes,
        "use_inter_op_parallelism", use_inter_op_parallelism)
      return _result
    except _core._FallbackException:
      return parallel_map_dataset_eager_fallback(
          input_dataset, other_arguments, num_parallel_calls, f=f,
          output_types=output_types, output_shapes=output_shapes,
          use_inter_op_parallelism=use_inter_op_parallelism, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def parallel_map_dataset_eager_fallback(input_dataset, other_arguments, num_parallel_calls, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function parallel_map_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'parallel_map_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'parallel_map_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  if use_inter_op_parallelism is None:
    use_inter_op_parallelism = True
  use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int32)
  _inputs_flat = [input_dataset] + list(other_arguments) + [num_parallel_calls]
  _attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
  output_types, "output_shapes", output_shapes, "use_inter_op_parallelism",
  use_inter_op_parallelism)
  _result = _execute.execute(b"ParallelMapDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ParallelMapDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, name=None):
  r"""Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    num_parallel_calls: A `Tensor` of type `int64`.
    dense_defaults: A list of `Tensor` objects with types from: `float32`, `int64`, `string`.
      A dict mapping string keys to `Tensor`s.
      The keys of the dict must match the dense_keys of the feature.
    sparse_keys: A list of `strings`.
      A list of string keys in the examples features.
      The results for these keys will be returned as `SparseTensor` objects.
    dense_keys: A list of `strings`.
      A list of Ndense string Tensors (scalars).
      The keys expected in the Examples features associated with dense values.
    sparse_types: A list of `tf.DTypes` from: `tf.float32, tf.int64, tf.string`.
      A list of `DTypes` of the same length as `sparse_keys`.
      Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),
      and `tf.string` (`BytesList`) are supported.
    dense_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
      List of tuples with the same length as `dense_keys`.
      The shape of the data for each dense feature referenced by `dense_keys`.
      Required for any input tensors identified by `dense_keys`.  Must be
      either fully defined, or may contain an unknown first dimension.
      An unknown first dimension means the feature is treated as having
      a variable number of blocks, and the output shape along this dimension
      is considered unknown at graph build time.  Padding is applied for
      minibatch elements smaller than the maximum number of blocks for the
      given feature along this dimension.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
      The type list for the return values.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
      The list of shapes being produced.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(sparse_keys, (list, tuple)):
      raise TypeError(
          "Expected list for 'sparse_keys' argument to "
          "'parse_example_dataset' Op, not %r." % sparse_keys)
    sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys]
    if not isinstance(dense_keys, (list, tuple)):
      raise TypeError(
          "Expected list for 'dense_keys' argument to "
          "'parse_example_dataset' Op, not %r." % dense_keys)
    dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys]
    if not isinstance(sparse_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'sparse_types' argument to "
          "'parse_example_dataset' Op, not %r." % sparse_types)
    sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types]
    if not isinstance(dense_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'dense_shapes' argument to "
          "'parse_example_dataset' Op, not %r." % dense_shapes)
    dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes]
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'parse_example_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'parse_example_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ParseExampleDataset", input_dataset=input_dataset,
        num_parallel_calls=num_parallel_calls, dense_defaults=dense_defaults,
        sparse_keys=sparse_keys, dense_keys=dense_keys,
        sparse_types=sparse_types, dense_shapes=dense_shapes,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("sparse_keys", _op.get_attr("sparse_keys"), "dense_keys",
              _op.get_attr("dense_keys"), "sparse_types",
              _op.get_attr("sparse_types"), "Tdense", _op.get_attr("Tdense"),
              "dense_shapes", _op.get_attr("dense_shapes"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ParseExampleDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ParseExampleDataset", name, _ctx._post_execution_callbacks,
        input_dataset, num_parallel_calls, dense_defaults, "sparse_keys",
        sparse_keys, "dense_keys", dense_keys, "sparse_types", sparse_types,
        "dense_shapes", dense_shapes, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return parse_example_dataset_eager_fallback(
          input_dataset, num_parallel_calls, dense_defaults,
          sparse_keys=sparse_keys, dense_keys=dense_keys,
          sparse_types=sparse_types, dense_shapes=dense_shapes,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def parse_example_dataset_eager_fallback(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function parse_example_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(sparse_keys, (list, tuple)):
    raise TypeError(
        "Expected list for 'sparse_keys' argument to "
        "'parse_example_dataset' Op, not %r." % sparse_keys)
  sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys]
  if not isinstance(dense_keys, (list, tuple)):
    raise TypeError(
        "Expected list for 'dense_keys' argument to "
        "'parse_example_dataset' Op, not %r." % dense_keys)
  dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys]
  if not isinstance(sparse_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'sparse_types' argument to "
        "'parse_example_dataset' Op, not %r." % sparse_types)
  sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types]
  if not isinstance(dense_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'dense_shapes' argument to "
        "'parse_example_dataset' Op, not %r." % dense_shapes)
  dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes]
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'parse_example_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'parse_example_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Tdense, dense_defaults = _execute.convert_to_mixed_eager_tensors(dense_defaults, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
  _inputs_flat = [input_dataset, num_parallel_calls] + list(dense_defaults)
  _attrs = ("sparse_keys", sparse_keys, "dense_keys", dense_keys,
  "sparse_types", sparse_types, "Tdense", _attr_Tdense, "dense_shapes",
  dense_shapes, "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ParseExampleDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ParseExampleDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def prefetch_dataset(input_dataset, buffer_size, output_types, output_shapes, name=None):
  r"""Creates a dataset that asynchronously prefetches elements from `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    buffer_size: A `Tensor` of type `int64`.
      The maximum number of elements to buffer in an iterator over
      this dataset.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'prefetch_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'prefetch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "PrefetchDataset", input_dataset=input_dataset,
        buffer_size=buffer_size, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "PrefetchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "PrefetchDataset", name, _ctx._post_execution_callbacks,
        input_dataset, buffer_size, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return prefetch_dataset_eager_fallback(
          input_dataset, buffer_size, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def prefetch_dataset_eager_fallback(input_dataset, buffer_size, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function prefetch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'prefetch_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'prefetch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  _inputs_flat = [input_dataset, buffer_size]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"PrefetchDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "PrefetchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def prepend_from_queue_and_padded_batch_dataset(input_dataset, batch_size, padded_shapes, padding_values, output_shapes, name=None):
  r"""TODO: add doc.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    batch_size: A `Tensor` of type `int64`.
    padded_shapes: A list of at least 1 `Tensor` objects with type `int64`.
    padding_values: A list of `Tensor` objects.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(padded_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'padded_shapes' argument to "
          "'prepend_from_queue_and_padded_batch_dataset' Op, not %r." % padded_shapes)
    _attr_N = len(padded_shapes)
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'prepend_from_queue_and_padded_batch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "PrependFromQueueAndPaddedBatchDataset", input_dataset=input_dataset,
        batch_size=batch_size, padded_shapes=padded_shapes,
        padding_values=padding_values, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"), "output_shapes",
              _op.get_attr("output_shapes"), "N", _op.get_attr("N"))
    _execute.record_gradient(
      "PrependFromQueueAndPaddedBatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "PrependFromQueueAndPaddedBatchDataset", name,
        _ctx._post_execution_callbacks, input_dataset, batch_size,
        padded_shapes, padding_values, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return prepend_from_queue_and_padded_batch_dataset_eager_fallback(
          input_dataset, batch_size, padded_shapes, padding_values,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def prepend_from_queue_and_padded_batch_dataset_eager_fallback(input_dataset, batch_size, padded_shapes, padding_values, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function prepend_from_queue_and_padded_batch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(padded_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'padded_shapes' argument to "
        "'prepend_from_queue_and_padded_batch_dataset' Op, not %r." % padded_shapes)
  _attr_N = len(padded_shapes)
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'prepend_from_queue_and_padded_batch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Toutput_types, padding_values = _execute.convert_to_mixed_eager_tensors(padding_values, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
  padded_shapes = _ops.convert_n_to_tensor(padded_shapes, _dtypes.int64)
  _inputs_flat = [input_dataset, batch_size] + list(padded_shapes) + list(padding_values)
  _attrs = ("Toutput_types", _attr_Toutput_types, "output_shapes",
  output_shapes, "N", _attr_N)
  _result = _execute.execute(b"PrependFromQueueAndPaddedBatchDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "PrependFromQueueAndPaddedBatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def random_dataset(seed, seed2, output_types, output_shapes, name=None):
  r"""Creates a Dataset that returns pseudorandom numbers.

  Args:
    seed: A `Tensor` of type `int64`.
      A scalar seed for the random number generator. If either seed or
      seed2 is set to be non-zero, the random number generator is seeded
      by the given seed.  Otherwise, a random seed is used.
    seed2: A `Tensor` of type `int64`.
      A second scalar seed to avoid seed collision.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'random_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'random_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "RandomDataset", seed=seed, seed2=seed2, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "RandomDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "RandomDataset", name, _ctx._post_execution_callbacks, seed, seed2,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return random_dataset_eager_fallback(
          seed, seed2, output_types=output_types, output_shapes=output_shapes,
          name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def random_dataset_eager_fallback(seed, seed2, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function random_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'random_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'random_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  seed = _ops.convert_to_tensor(seed, _dtypes.int64)
  seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64)
  _inputs_flat = [seed, seed2]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"RandomDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "RandomDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def range_dataset(start, stop, step, output_types, output_shapes, name=None):
  r"""Creates a dataset with a range of values. Corresponds to python's xrange.

  Args:
    start: A `Tensor` of type `int64`.
      corresponds to start in python's xrange().
    stop: A `Tensor` of type `int64`.
      corresponds to stop in python's xrange().
    step: A `Tensor` of type `int64`.
      corresponds to step in python's xrange().
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'range_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'range_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "RangeDataset", start=start, stop=stop, step=step,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "RangeDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "RangeDataset",
        name, _ctx._post_execution_callbacks, start, stop, step,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return range_dataset_eager_fallback(
          start, stop, step, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def range_dataset_eager_fallback(start, stop, step, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function range_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'range_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'range_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  start = _ops.convert_to_tensor(start, _dtypes.int64)
  stop = _ops.convert_to_tensor(stop, _dtypes.int64)
  step = _ops.convert_to_tensor(step, _dtypes.int64)
  _inputs_flat = [start, stop, step]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"RangeDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "RangeDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def reduce_dataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None):
  r"""Reduces the input dataset to a singleton using a reduce function.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    initial_state: A list of `Tensor` objects.
      A nested structure of tensors, representing the initial state of the
      transformation.
    other_arguments: A list of `Tensor` objects.
    f: A function decorated with @Defun.
      A function that maps `(old_state, input_element)` to `new_state`. It must take
      two arguments and return a nested structures of tensors. The structure of
      `new_state` must match the structure of `initial_state`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    use_inter_op_parallelism: An optional `bool`. Defaults to `True`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `output_types`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'reduce_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'reduce_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    if use_inter_op_parallelism is None:
      use_inter_op_parallelism = True
    use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
    _, _, _op = _op_def_lib._apply_op_helper(
        "ReduceDataset", input_dataset=input_dataset,
        initial_state=initial_state, other_arguments=other_arguments, f=f,
        output_types=output_types, output_shapes=output_shapes,
        use_inter_op_parallelism=use_inter_op_parallelism, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Tstate", _op.get_attr("Tstate"),
              "Targuments", _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "use_inter_op_parallelism",
              _op.get_attr("use_inter_op_parallelism"))
    _execute.record_gradient(
      "ReduceDataset", _inputs_flat, _attrs, _result, name)
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ReduceDataset", name, _ctx._post_execution_callbacks, input_dataset,
        initial_state, other_arguments, "f", f, "output_types", output_types,
        "output_shapes", output_shapes, "use_inter_op_parallelism",
        use_inter_op_parallelism)
      return _result
    except _core._FallbackException:
      return reduce_dataset_eager_fallback(
          input_dataset, initial_state, other_arguments, f=f,
          output_types=output_types, output_shapes=output_shapes,
          use_inter_op_parallelism=use_inter_op_parallelism, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def reduce_dataset_eager_fallback(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function reduce_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'reduce_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'reduce_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  if use_inter_op_parallelism is None:
    use_inter_op_parallelism = True
  use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
  _attr_Tstate, initial_state = _execute.convert_to_mixed_eager_tensors(initial_state, _ctx)
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(initial_state) + list(other_arguments)
  _attrs = ("f", f, "Tstate", _attr_Tstate, "Targuments", _attr_Targuments,
  "output_types", output_types, "output_shapes", output_shapes,
  "use_inter_op_parallelism", use_inter_op_parallelism)
  _result = _execute.execute(b"ReduceDataset", len(output_types),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "ReduceDataset", _inputs_flat, _attrs, _result, name)
  return _result


def repeat_dataset(input_dataset, count, output_types, output_shapes, name=None):
  r"""Creates a dataset that emits the outputs of `input_dataset` `count` times.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    count: A `Tensor` of type `int64`.
      A scalar representing the number of times that `input_dataset` should
      be repeated. A value of `-1` indicates that it should be repeated infinitely.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'repeat_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'repeat_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "RepeatDataset", input_dataset=input_dataset, count=count,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "RepeatDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "RepeatDataset", name, _ctx._post_execution_callbacks, input_dataset,
        count, "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return repeat_dataset_eager_fallback(
          input_dataset, count, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def repeat_dataset_eager_fallback(input_dataset, count, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function repeat_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'repeat_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'repeat_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  count = _ops.convert_to_tensor(count, _dtypes.int64)
  _inputs_flat = [input_dataset, count]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"RepeatDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "RepeatDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def scan_dataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, name=None):
  r"""Creates a dataset successively reduces `f` over the elements of `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    initial_state: A list of `Tensor` objects.
    other_arguments: A list of `Tensor` objects.
    f: A function decorated with @Defun.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'scan_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'scan_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ScanDataset", input_dataset=input_dataset,
        initial_state=initial_state, other_arguments=other_arguments, f=f,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("f", _op.get_attr("f"), "Tstate", _op.get_attr("Tstate"),
              "Targuments", _op.get_attr("Targuments"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ScanDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "ScanDataset",
        name, _ctx._post_execution_callbacks, input_dataset, initial_state,
        other_arguments, "f", f, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return scan_dataset_eager_fallback(
          input_dataset, initial_state, other_arguments, f=f,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def scan_dataset_eager_fallback(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function scan_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'scan_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'scan_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Tstate, initial_state = _execute.convert_to_mixed_eager_tensors(initial_state, _ctx)
  _attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset] + list(initial_state) + list(other_arguments)
  _attrs = ("f", f, "Tstate", _attr_Tstate, "Targuments", _attr_Targuments,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ScanDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ScanDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def serialize_iterator(resource_handle, name=None):
  r"""Converts the given `resource_handle` representing an iterator to a variant tensor.

  Args:
    resource_handle: A `Tensor` of type `resource`.
      A handle to an iterator resource.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "SerializeIterator", resource_handle=resource_handle, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "SerializeIterator", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "SerializeIterator", name, _ctx._post_execution_callbacks,
        resource_handle)
      return _result
    except _core._FallbackException:
      return serialize_iterator_eager_fallback(
          resource_handle, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def serialize_iterator_eager_fallback(resource_handle, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function serialize_iterator
  """
  _ctx = ctx if ctx else _context.context()
  resource_handle = _ops.convert_to_tensor(resource_handle, _dtypes.resource)
  _inputs_flat = [resource_handle]
  _attrs = None
  _result = _execute.execute(b"SerializeIterator", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SerializeIterator", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def set_stats_aggregator_dataset(input_dataset, stats_aggregator, output_types, output_shapes, name=None):
  r"""TODO: add doc.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    stats_aggregator: A `Tensor` of type `resource`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'set_stats_aggregator_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'set_stats_aggregator_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "SetStatsAggregatorDataset", input_dataset=input_dataset,
        stats_aggregator=stats_aggregator, output_types=output_types,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "SetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "SetStatsAggregatorDataset", name, _ctx._post_execution_callbacks,
        input_dataset, stats_aggregator, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return set_stats_aggregator_dataset_eager_fallback(
          input_dataset, stats_aggregator, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def set_stats_aggregator_dataset_eager_fallback(input_dataset, stats_aggregator, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function set_stats_aggregator_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'set_stats_aggregator_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'set_stats_aggregator_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  stats_aggregator = _ops.convert_to_tensor(stats_aggregator, _dtypes.resource)
  _inputs_flat = [input_dataset, stats_aggregator]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"SetStatsAggregatorDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "SetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def shuffle_and_repeat_dataset(input_dataset, buffer_size, seed, seed2, count, output_types, output_shapes, name=None):
  r"""Creates a dataset that shuffles and repeats elements from `input_dataset`

  pseudorandomly.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    buffer_size: A `Tensor` of type `int64`.
      The number of output elements to buffer in an iterator over
      this dataset. Compare with the `min_after_dequeue` attr when creating a
      `RandomShuffleQueue`.
    seed: A `Tensor` of type `int64`.
      A scalar seed for the random number generator. If either `seed` or
      `seed2` is set to be non-zero, the random number generator is seeded
      by the given seed.  Otherwise, a random seed is used.
    seed2: A `Tensor` of type `int64`.
      A second scalar seed to avoid seed collision.
    count: A `Tensor` of type `int64`.
      A scalar representing the number of times the underlying dataset
      should be repeated. The default is `-1`, which results in infinite repetition.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'shuffle_and_repeat_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'shuffle_and_repeat_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ShuffleAndRepeatDataset", input_dataset=input_dataset,
        buffer_size=buffer_size, seed=seed, seed2=seed2, count=count,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ShuffleAndRepeatDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ShuffleAndRepeatDataset", name, _ctx._post_execution_callbacks,
        input_dataset, buffer_size, seed, seed2, count, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return shuffle_and_repeat_dataset_eager_fallback(
          input_dataset, buffer_size, seed, seed2, count,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def shuffle_and_repeat_dataset_eager_fallback(input_dataset, buffer_size, seed, seed2, count, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function shuffle_and_repeat_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'shuffle_and_repeat_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'shuffle_and_repeat_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  seed = _ops.convert_to_tensor(seed, _dtypes.int64)
  seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64)
  count = _ops.convert_to_tensor(count, _dtypes.int64)
  _inputs_flat = [input_dataset, buffer_size, seed, seed2, count]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ShuffleAndRepeatDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "ShuffleAndRepeatDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def shuffle_dataset(input_dataset, buffer_size, seed, seed2, output_types, output_shapes, reshuffle_each_iteration=True, name=None):
  r"""Creates a dataset that shuffles elements from `input_dataset` pseudorandomly.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    buffer_size: A `Tensor` of type `int64`.
      The number of output elements to buffer in an iterator over
      this dataset. Compare with the `min_after_dequeue` attr when creating a
      `RandomShuffleQueue`.
    seed: A `Tensor` of type `int64`.
      A scalar seed for the random number generator. If either `seed` or
      `seed2` is set to be non-zero, the random number generator is seeded
      by the given seed.  Otherwise, a random seed is used.
    seed2: A `Tensor` of type `int64`.
      A second scalar seed to avoid seed collision.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    reshuffle_each_iteration: An optional `bool`. Defaults to `True`.
      If true, each iterator over this dataset will be given
      a different pseudorandomly generated seed, based on a sequence seeded by the
      `seed` and `seed2` inputs. If false, each iterator will be given the same
      seed, and repeated iteration over this dataset will yield the exact same
      sequence of results.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'shuffle_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'shuffle_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    if reshuffle_each_iteration is None:
      reshuffle_each_iteration = True
    reshuffle_each_iteration = _execute.make_bool(reshuffle_each_iteration, "reshuffle_each_iteration")
    _, _, _op = _op_def_lib._apply_op_helper(
        "ShuffleDataset", input_dataset=input_dataset,
        buffer_size=buffer_size, seed=seed, seed2=seed2,
        output_types=output_types, output_shapes=output_shapes,
        reshuffle_each_iteration=reshuffle_each_iteration, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("reshuffle_each_iteration",
              _op.get_attr("reshuffle_each_iteration"), "output_types",
              _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "ShuffleDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "ShuffleDataset", name, _ctx._post_execution_callbacks, input_dataset,
        buffer_size, seed, seed2, "reshuffle_each_iteration",
        reshuffle_each_iteration, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return shuffle_dataset_eager_fallback(
          input_dataset, buffer_size, seed, seed2,
          reshuffle_each_iteration=reshuffle_each_iteration,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def shuffle_dataset_eager_fallback(input_dataset, buffer_size, seed, seed2, output_types, output_shapes, reshuffle_each_iteration=True, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function shuffle_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'shuffle_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'shuffle_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  if reshuffle_each_iteration is None:
    reshuffle_each_iteration = True
  reshuffle_each_iteration = _execute.make_bool(reshuffle_each_iteration, "reshuffle_each_iteration")
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  seed = _ops.convert_to_tensor(seed, _dtypes.int64)
  seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64)
  _inputs_flat = [input_dataset, buffer_size, seed, seed2]
  _attrs = ("reshuffle_each_iteration", reshuffle_each_iteration,
  "output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"ShuffleDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ShuffleDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def sink_dataset(input_dataset, name=None):
  r"""A placeholder for input pipeline graph optimizations.

  A placeholder for input pipeline graph optimizations.

  Args:
    input_dataset: A `Tensor` of type `variant`.
      A variant tensor representing the input dataset.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "SinkDataset", input_dataset=input_dataset, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "SinkDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "SinkDataset",
        name, _ctx._post_execution_callbacks, input_dataset)
      return _result
    except _core._FallbackException:
      return sink_dataset_eager_fallback(
          input_dataset, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def sink_dataset_eager_fallback(input_dataset, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function sink_dataset
  """
  _ctx = ctx if ctx else _context.context()
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset]
  _attrs = None
  _result = _execute.execute(b"SinkDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SinkDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def skip_dataset(input_dataset, count, output_types, output_shapes, name=None):
  r"""Creates a dataset that skips `count` elements from the `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    count: A `Tensor` of type `int64`.
      A scalar representing the number of elements from the `input_dataset`
      that should be skipped.  If count is -1, skips everything.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'skip_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'skip_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "SkipDataset", input_dataset=input_dataset, count=count,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "SkipDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "SkipDataset",
        name, _ctx._post_execution_callbacks, input_dataset, count,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return skip_dataset_eager_fallback(
          input_dataset, count, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def skip_dataset_eager_fallback(input_dataset, count, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function skip_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'skip_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'skip_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  count = _ops.convert_to_tensor(count, _dtypes.int64)
  _inputs_flat = [input_dataset, count]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"SkipDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SkipDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def slide_dataset(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None):
  r"""Creates a dataset that passes a sliding window over `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    window_size: A `Tensor` of type `int64`.
      A scalar representing the number of elements in the
      sliding window.
    window_shift: A `Tensor` of type `int64`.
      A scalar representing the steps moving the sliding window
      forward in one iteration. It must be positive.
    window_stride: A `Tensor` of type `int64`.
      A scalar representing the stride of the input elements of the sliding window.
      It must be positive.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'slide_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'slide_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "SlideDataset", input_dataset=input_dataset, window_size=window_size,
        window_shift=window_shift, window_stride=window_stride,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "SlideDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "SlideDataset",
        name, _ctx._post_execution_callbacks, input_dataset, window_size,
        window_shift, window_stride, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return slide_dataset_eager_fallback(
          input_dataset, window_size, window_shift, window_stride,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def slide_dataset_eager_fallback(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function slide_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'slide_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'slide_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  window_size = _ops.convert_to_tensor(window_size, _dtypes.int64)
  window_shift = _ops.convert_to_tensor(window_shift, _dtypes.int64)
  window_stride = _ops.convert_to_tensor(window_stride, _dtypes.int64)
  _inputs_flat = [input_dataset, window_size, window_shift, window_stride]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"SlideDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SlideDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def sparse_tensor_slice_dataset(indices, values, dense_shape, name=None):
  r"""Creates a dataset that splits a SparseTensor into elements row-wise.

  Args:
    indices: A `Tensor` of type `int64`.
    values: A `Tensor`.
    dense_shape: A `Tensor` of type `int64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "SparseTensorSliceDataset", indices=indices, values=values,
        dense_shape=dense_shape, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Tvalues", _op.get_attr("Tvalues"))
    _execute.record_gradient(
      "SparseTensorSliceDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "SparseTensorSliceDataset", name, _ctx._post_execution_callbacks,
        indices, values, dense_shape)
      return _result
    except _core._FallbackException:
      return sparse_tensor_slice_dataset_eager_fallback(
          indices, values, dense_shape, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def sparse_tensor_slice_dataset_eager_fallback(indices, values, dense_shape, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function sparse_tensor_slice_dataset
  """
  _ctx = ctx if ctx else _context.context()
  _attr_Tvalues, (values,) = _execute.args_to_matching_eager([values], _ctx)
  indices = _ops.convert_to_tensor(indices, _dtypes.int64)
  dense_shape = _ops.convert_to_tensor(dense_shape, _dtypes.int64)
  _inputs_flat = [indices, values, dense_shape]
  _attrs = ("Tvalues", _attr_Tvalues)
  _result = _execute.execute(b"SparseTensorSliceDataset", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "SparseTensorSliceDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def sql_dataset(driver_name, data_source_name, query, output_types, output_shapes, name=None):
  r"""Creates a dataset that executes a SQL query and emits rows of the result set.

  Args:
    driver_name: A `Tensor` of type `string`.
      The database type. Currently, the only supported type is 'sqlite'.
    data_source_name: A `Tensor` of type `string`.
      A connection string to connect to the database.
    query: A `Tensor` of type `string`. A SQL query to execute.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'sql_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'sql_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "SqlDataset", driver_name=driver_name,
        data_source_name=data_source_name, query=query,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "SqlDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "SqlDataset",
        name, _ctx._post_execution_callbacks, driver_name, data_source_name,
        query, "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return sql_dataset_eager_fallback(
          driver_name, data_source_name, query, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def sql_dataset_eager_fallback(driver_name, data_source_name, query, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function sql_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'sql_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'sql_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  driver_name = _ops.convert_to_tensor(driver_name, _dtypes.string)
  data_source_name = _ops.convert_to_tensor(data_source_name, _dtypes.string)
  query = _ops.convert_to_tensor(query, _dtypes.string)
  _inputs_flat = [driver_name, data_source_name, query]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"SqlDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SqlDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stats_aggregator_handle(container="", shared_name="", name=None):
  r"""Creates a statistics manager resource.

  Args:
    container: An optional `string`. Defaults to `""`.
    shared_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `resource`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if container is None:
      container = ""
    container = _execute.make_str(container, "container")
    if shared_name is None:
      shared_name = ""
    shared_name = _execute.make_str(shared_name, "shared_name")
    _, _, _op = _op_def_lib._apply_op_helper(
        "StatsAggregatorHandle", container=container, shared_name=shared_name,
        name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("container", _op.get_attr("container"), "shared_name",
              _op.get_attr("shared_name"))
    _execute.record_gradient(
      "StatsAggregatorHandle", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "StatsAggregatorHandle", name, _ctx._post_execution_callbacks,
        "container", container, "shared_name", shared_name)
      return _result
    except _core._FallbackException:
      return stats_aggregator_handle_eager_fallback(
          container=container, shared_name=shared_name, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def stats_aggregator_handle_eager_fallback(container="", shared_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stats_aggregator_handle
  """
  _ctx = ctx if ctx else _context.context()
  if container is None:
    container = ""
  container = _execute.make_str(container, "container")
  if shared_name is None:
    shared_name = ""
  shared_name = _execute.make_str(shared_name, "shared_name")
  _inputs_flat = []
  _attrs = ("container", container, "shared_name", shared_name)
  _result = _execute.execute(b"StatsAggregatorHandle", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "StatsAggregatorHandle", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stats_aggregator_summary(iterator, name=None):
  r"""Produces a summary of any statistics recorded by the given statistics manager.

  Args:
    iterator: A `Tensor` of type `resource`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "StatsAggregatorSummary", iterator=iterator, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "StatsAggregatorSummary", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "StatsAggregatorSummary", name, _ctx._post_execution_callbacks,
        iterator)
      return _result
    except _core._FallbackException:
      return stats_aggregator_summary_eager_fallback(
          iterator, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def stats_aggregator_summary_eager_fallback(iterator, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stats_aggregator_summary
  """
  _ctx = ctx if ctx else _context.context()
  iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
  _inputs_flat = [iterator]
  _attrs = None
  _result = _execute.execute(b"StatsAggregatorSummary", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatsAggregatorSummary", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def tf_record_dataset(filenames, compression_type, buffer_size, name=None):
  r"""Creates a dataset that emits the records from one or more TFRecord files.

  Args:
    filenames: A `Tensor` of type `string`.
      A scalar or vector containing the name(s) of the file(s) to be
      read.
    compression_type: A `Tensor` of type `string`.
      A scalar containing either (i) the empty string (no
      compression), (ii) "ZLIB", or (iii) "GZIP".
    buffer_size: A `Tensor` of type `int64`.
      A scalar representing the number of bytes to buffer. A value of
      0 means no buffering will be performed.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "TFRecordDataset", filenames=filenames,
        compression_type=compression_type, buffer_size=buffer_size, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "TFRecordDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "TFRecordDataset", name, _ctx._post_execution_callbacks, filenames,
        compression_type, buffer_size)
      return _result
    except _core._FallbackException:
      return tf_record_dataset_eager_fallback(
          filenames, compression_type, buffer_size, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def tf_record_dataset_eager_fallback(filenames, compression_type, buffer_size, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tf_record_dataset
  """
  _ctx = ctx if ctx else _context.context()
  filenames = _ops.convert_to_tensor(filenames, _dtypes.string)
  compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  _inputs_flat = [filenames, compression_type, buffer_size]
  _attrs = None
  _result = _execute.execute(b"TFRecordDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TFRecordDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def take_dataset(input_dataset, count, output_types, output_shapes, name=None):
  r"""Creates a dataset that contains `count` elements from the `input_dataset`.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    count: A `Tensor` of type `int64`.
      A scalar representing the number of elements from the `input_dataset`
      that should be taken. A value of `-1` indicates that all of `input_dataset`
      is taken.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'take_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'take_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "TakeDataset", input_dataset=input_dataset, count=count,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "TakeDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "TakeDataset",
        name, _ctx._post_execution_callbacks, input_dataset, count,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return take_dataset_eager_fallback(
          input_dataset, count, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def take_dataset_eager_fallback(input_dataset, count, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function take_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'take_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'take_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  count = _ops.convert_to_tensor(count, _dtypes.int64)
  _inputs_flat = [input_dataset, count]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"TakeDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TakeDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def tensor_dataset(components, output_shapes, name=None):
  r"""Creates a dataset that emits `components` as a tuple of tensors once.

  Args:
    components: A list of `Tensor` objects.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'tensor_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "TensorDataset", components=components, output_shapes=output_shapes,
        name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "TensorDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "TensorDataset", name, _ctx._post_execution_callbacks, components,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return tensor_dataset_eager_fallback(
          components, output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def tensor_dataset_eager_fallback(components, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tensor_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'tensor_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Toutput_types, components = _execute.convert_to_mixed_eager_tensors(components, _ctx)
  _inputs_flat = list(components)
  _attrs = ("Toutput_types", _attr_Toutput_types, "output_shapes",
  output_shapes)
  _result = _execute.execute(b"TensorDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TensorDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def tensor_slice_dataset(components, output_shapes, name=None):
  r"""Creates a dataset that emits each dim-0 slice of `components` once.

  Args:
    components: A list of `Tensor` objects.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'tensor_slice_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "TensorSliceDataset", components=components,
        output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("Toutput_types", _op.get_attr("Toutput_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "TensorSliceDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "TensorSliceDataset", name, _ctx._post_execution_callbacks,
        components, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return tensor_slice_dataset_eager_fallback(
          components, output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def tensor_slice_dataset_eager_fallback(components, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tensor_slice_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'tensor_slice_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  _attr_Toutput_types, components = _execute.convert_to_mixed_eager_tensors(components, _ctx)
  _inputs_flat = list(components)
  _attrs = ("Toutput_types", _attr_Toutput_types, "output_shapes",
  output_shapes)
  _result = _execute.execute(b"TensorSliceDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TensorSliceDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def text_line_dataset(filenames, compression_type, buffer_size, name=None):
  r"""Creates a dataset that emits the lines of one or more text files.

  Args:
    filenames: A `Tensor` of type `string`.
      A scalar or a vector containing the name(s) of the file(s) to be
      read.
    compression_type: A `Tensor` of type `string`.
      A scalar containing either (i) the empty string (no
      compression), (ii) "ZLIB", or (iii) "GZIP".
    buffer_size: A `Tensor` of type `int64`.
      A scalar containing the number of bytes to buffer.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "TextLineDataset", filenames=filenames,
        compression_type=compression_type, buffer_size=buffer_size, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient(
      "TextLineDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "TextLineDataset", name, _ctx._post_execution_callbacks, filenames,
        compression_type, buffer_size)
      return _result
    except _core._FallbackException:
      return text_line_dataset_eager_fallback(
          filenames, compression_type, buffer_size, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def text_line_dataset_eager_fallback(filenames, compression_type, buffer_size, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function text_line_dataset
  """
  _ctx = ctx if ctx else _context.context()
  filenames = _ops.convert_to_tensor(filenames, _dtypes.string)
  compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string)
  buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
  _inputs_flat = [filenames, compression_type, buffer_size]
  _attrs = None
  _result = _execute.execute(b"TextLineDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TextLineDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def unbatch_dataset(input_dataset, output_types, output_shapes, name=None):
  r"""A dataset that splits the elements of its input into multiple elements.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'unbatch_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'unbatch_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "UnbatchDataset", input_dataset=input_dataset,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "UnbatchDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "UnbatchDataset", name, _ctx._post_execution_callbacks, input_dataset,
        "output_types", output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return unbatch_dataset_eager_fallback(
          input_dataset, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def unbatch_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function unbatch_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'unbatch_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'unbatch_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  _inputs_flat = [input_dataset]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"UnbatchDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "UnbatchDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def window_dataset(input_dataset, size, shift, stride, drop_remainder, output_types, output_shapes, name=None):
  r"""A dataset that creates window datasets from the input dataset.

  Args:
    input_dataset: A `Tensor` of type `variant`.
    size: A `Tensor` of type `int64`.
      A scalar representing the number of elements to accumulate in a window.
    shift: A `Tensor` of type `int64`.
      A scalar representing the steps moving the sliding window forward in one
      iteration. It must be positive.
    stride: A `Tensor` of type `int64`.
      A scalar representing the stride of the input elements of the sliding window.
      It must be positive.
    drop_remainder: A `Tensor` of type `bool`.
      A scalar representing whether a window should be dropped in case its size is
      smaller than desired.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'window_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'window_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "WindowDataset", input_dataset=input_dataset, size=size, shift=shift,
        stride=stride, drop_remainder=drop_remainder,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"))
    _execute.record_gradient(
      "WindowDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name,
        "WindowDataset", name, _ctx._post_execution_callbacks, input_dataset,
        size, shift, stride, drop_remainder, "output_types", output_types,
        "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return window_dataset_eager_fallback(
          input_dataset, size, shift, stride, drop_remainder,
          output_types=output_types, output_shapes=output_shapes, name=name,
          ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def window_dataset_eager_fallback(input_dataset, size, shift, stride, drop_remainder, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function window_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'window_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'window_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
  size = _ops.convert_to_tensor(size, _dtypes.int64)
  shift = _ops.convert_to_tensor(shift, _dtypes.int64)
  stride = _ops.convert_to_tensor(stride, _dtypes.int64)
  drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
  _inputs_flat = [input_dataset, size, shift, stride, drop_remainder]
  _attrs = ("output_types", output_types, "output_shapes", output_shapes)
  _result = _execute.execute(b"WindowDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "WindowDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def zip_dataset(input_datasets, output_types, output_shapes, name=None):
  r"""Creates a dataset that zips together `input_datasets`.

  Args:
    input_datasets: A list of at least 1 `Tensor` objects with type `variant`.
    output_types: A list of `tf.DTypes` that has length `>= 1`.
    output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    if not isinstance(input_datasets, (list, tuple)):
      raise TypeError(
          "Expected list for 'input_datasets' argument to "
          "'zip_dataset' Op, not %r." % input_datasets)
    _attr_N = len(input_datasets)
    if not isinstance(output_types, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_types' argument to "
          "'zip_dataset' Op, not %r." % output_types)
    output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'zip_dataset' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _, _, _op = _op_def_lib._apply_op_helper(
        "ZipDataset", input_datasets=input_datasets,
        output_types=output_types, output_shapes=output_shapes, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
              _op.get_attr("output_shapes"), "N", _op.get_attr("N"))
    _execute.record_gradient(
      "ZipDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "ZipDataset",
        name, _ctx._post_execution_callbacks, input_datasets, "output_types",
        output_types, "output_shapes", output_shapes)
      return _result
    except _core._FallbackException:
      return zip_dataset_eager_fallback(
          input_datasets, output_types=output_types,
          output_shapes=output_shapes, name=name, ctx=_ctx)
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)


def zip_dataset_eager_fallback(input_datasets, output_types, output_shapes, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function zip_dataset
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(input_datasets, (list, tuple)):
    raise TypeError(
        "Expected list for 'input_datasets' argument to "
        "'zip_dataset' Op, not %r." % input_datasets)
  _attr_N = len(input_datasets)
  if not isinstance(output_types, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_types' argument to "
        "'zip_dataset' Op, not %r." % output_types)
  output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
  if not isinstance(output_shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'output_shapes' argument to "
        "'zip_dataset' Op, not %r." % output_shapes)
  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
  input_datasets = _ops.convert_n_to_tensor(input_datasets, _dtypes.variant)
  _inputs_flat = list(input_datasets)
  _attrs = ("output_types", output_types, "output_shapes", output_shapes, "N",
  _attr_N)
  _result = _execute.execute(b"ZipDataset", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "ZipDataset", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
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_op_def_lib = _InitOpDefLibrary(b"\nd\n\021AnonymousIterator\032\n\n\006handle\030\024\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\177\n\014BatchDataset\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\225\001\n\016BatchDatasetV2\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\205\001\n\031BytesProducedStatsDataset\022\021\n\rinput_dataset\030\025\022\007\n\003tag\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n}\n\014CacheDataset\022\021\n\rinput_dataset\030\025\022\014\n\010filename\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\212\001\n\022ConcatenateDataset\022\021\n\rinput_dataset\030\025\022\023\n\017another_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n.\n\016DatasetToGraph\022\021\n\rinput_dataset\030\025\032\t\n\005graph\030\007\n\203\001\n\026DatasetToSingleElement\022\013\n\007dataset\030\025\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\nJ\n\021DatasetToTFRecord\022\021\n\rinput_dataset\030\025\022\014\n\010filename\030\007\022\024\n\020compression_type\030\007\n\233\001\n\031DenseToSparseBatchDataset\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\r\n\trow_shape\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n=\n\023DeserializeIterator\022\023\n\017resource_handle\030\024\022\016\n\nserialized\030\025\210\001\001\n_\n\025EnqueueInQueueDataset\022\t\n\005queue\030\025\022\031\n\ncomponents2\013Tcomponents\"\035\n\013Tcomponents\022\nlist(type)(\0010\001\210\001\001\n\177\n\034FilterByLastComponentDataset\022\021\n\rinput_dataset\030\025\032\n\n\006output\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\276\001\n\rFilterDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\021\n\tpredicate\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\177\n\030FixedLengthRecordDataset\022\r\n\tfilenames\030\007\022\020\n\014header_bytes\030\t\022\020\n\014record_bytes\030\t\022\020\n\014footer_bytes\030\t\022\017\n\013buffer_size\030\t\032\n\n\006handle\030\025\210\001\001\n\267\001\n\016FlatMapDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\212\003\n\020GeneratorDataset\022\'\n\024init_func_other_args2\017Tinit_func_args\022\'\n\024next_func_other_args2\017Tnext_func_args\022/\n\030finalize_func_other_args2\023Tfinalize_func_args\032\n\n\006handle\030\025\"\021\n\tinit_func\022\004func\"\021\n\tnext_func\022\004func\"\025\n\rfinalize_func\022\004func\"\037\n\017Tinit_func_args\022\nlist(type)(\001\"\037\n\017Tnext_func_args\022\nlist(type)(\001\"#\n\023Tfinalize_func_args\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\357\004\n\025GroupByReducerDataset\022\021\n\rinput_dataset\030\025\0225\n\030key_func_other_arguments2\031Tkey_func_other_arguments\0227\n\031init_func_other_arguments2\032Tinit_func_other_arguments\022;\n\033reduce_func_other_arguments2\034Treduce_func_other_arguments\022?\n\035finalize_func_other_arguments2\036Tfinalize_func_other_arguments\032\n\n\006handle\030\025\"\020\n\010key_func\022\004func\"\021\n\tinit_func\022\004func\"\023\n\013reduce_func\022\004func\"\025\n\rfinalize_func\022\004func\")\n\031Tkey_func_other_arguments\022\nlist(type)(\001\"*\n\032Tinit_func_other_arguments\022\nlist(type)(\001\",\n\034Treduce_func_other_arguments\022\nlist(type)(\001\".\n\036Tfinalize_func_other_arguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\377\003\n\024GroupByWindowDataset\022\021\n\rinput_dataset\030\025\0225\n\030key_func_other_arguments2\031Tkey_func_other_arguments\022;\n\033reduce_func_other_arguments2\034Treduce_func_other_arguments\022E\n window_size_func_other_arguments2!Twindow_size_func_other_arguments\032\n\n\006handle\030\025\"\020\n\010key_func\022\004func\"\023\n\013reduce_func\022\004func\"\030\n\020window_size_func\022\004func\")\n\031Tkey_func_other_arguments\022\nlist(type)(\001\",\n\034Treduce_func_other_arguments\022\nlist(type)(\001\"1\n!Twindow_size_func_other_arguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\336\001\n\021InterleaveDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\020\n\014cycle_length\030\t\022\020\n\014block_length\030\t\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\207\001\n\010Iterator\032\n\n\006handle\030\024\"\025\n\013shared_name\022\006string\"\023\n\tcontainer\022\006string\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\213\001\n\030IteratorFromStringHandle\022\021\n\rstring_handle\030\007\032\023\n\017resource_handle\030\024\" \n\014output_types\022\nlist(type)\032\002\n\000(\001\"\"\n\routput_shapes\022\013list(shape)\032\002\n\000(\001\210\001\001\n\215\001\n\032IteratorFromStringHandleV2\022\021\n\rstring_handle\030\007\032\023\n\017resource_handle\030\024\" \n\014output_types\022\nlist(type)\032\002\n\000(\001\"\"\n\routput_shapes\022\013list(shape)\032\002\n\000(\001\210\001\001\n\200\001\n\017IteratorGetNext\022\014\n\010iterator\030\024\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n|\n\031IteratorGetNextAsOptional\022\014\n\010iterator\030\024\032\014\n\010optional\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\204\001\n\023IteratorGetNextSync\022\014\n\010iterator\030\024\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nC\n\026IteratorToStringHandle\022\023\n\017resource_handle\030\024\032\021\n\rstring_handle\030\007\210\001\001\n\211\001\n\nIteratorV2\032\n\n\006handle\030\024\"\025\n\013shared_name\022\006string\"\023\n\tcontainer\022\006string\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\177\n\023LatencyStatsDataset\022\021\n\rinput_dataset\030\025\022\007\n\003tag\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n,\n\014MakeIterator\022\013\n\007dataset\030\025\022\014\n\010iterator\030\024\210\001\001\n\371\001\n\022MapAndBatchDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\016\n\nbatch_size\030\t\022\030\n\024num_parallel_batches\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\371\001\n\024MapAndBatchDatasetV2\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\016\n\nbatch_size\030\t\022\026\n\022num_parallel_calls\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\331\001\n\nMapDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"$\n\030use_inter_op_parallelism\022\004bool\032\002(\001\n\246\001\n\010MapDefun\022\027\n\targuments2\nTarguments\032\026\n\006output2\014output_types\"\034\n\nTarguments\022\nlist(type)(\0010\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\t\n\001f\022\004func\no\n\014ModelDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\257\001\n\023MultiDeviceIterator\032\n\n\006handle\030\024\"\033\n\007devices\022\014list(string)(\0010\001\"\025\n\013shared_name\022\006string\"\023\n\tcontainer\022\006string\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\234\001\n#MultiDeviceIteratorFromStringHandle\022\021\n\rstring_handle\030\007\032\031\n\025multi_device_iterator\030\024\" \n\014output_types\022\nlist(type)\032\002\n\000(\001\"\"\n\routput_shapes\022\013list(shape)\032\002\n\000(\001\210\001\001\n\304\001\n#MultiDeviceIteratorGetNextFromShard\022\031\n\025multi_device_iterator\030\024\022\r\n\tshard_num\030\003\022\022\n\016incarnation_id\030\t\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nm\n\027MultiDeviceIteratorInit\022\013\n\007dataset\030\025\022\031\n\025multi_device_iterator\030\024\022\023\n\017max_buffer_size\030\t\032\022\n\016incarnation_id\030\t\210\001\001\nT\n!MultiDeviceIteratorToStringHandle\022\031\n\025multi_device_iterator\030\024\032\021\n\rstring_handle\030\007\210\001\001\n\257\001\n\017OneShotIterator\032\n\n\006handle\030\024\"\027\n\017dataset_factory\022\004func\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\n\205\001\n\017OptimizeDataset\022\021\n\rinput_dataset\030\025\022\021\n\roptimizations\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n_\n\021OptionalFromValue\022\033\n\ncomponents2\rToutput_types\032\014\n\010optional\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\n~\n\020OptionalGetValue\022\014\n\010optional\030\025\032\032\n\ncomponents2\014output_types\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n/\n\020OptionalHasValue\022\014\n\010optional\030\025\032\r\n\thas_value\030\n\n\034\n\014OptionalNone\032\014\n\010optional\030\025\n\313\001\n\022PaddedBatchDataset\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\024\n\rpadded_shapes\030\t*\001N\022\037\n\016padding_values2\rToutput_types\032\n\n\006handle\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\014\n\001N\022\003int(\0010\001\n\341\001\n\024PaddedBatchDatasetV2\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\024\n\rpadded_shapes\030\t*\001N\022\037\n\016padding_values2\rToutput_types\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\014\n\001N\022\003int(\0010\001\n\253\002\n\031ParallelInterleaveDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\020\n\014cycle_length\030\t\022\020\n\014block_length\030\t\022\n\n\006sloppy\030\n\022\032\n\026buffer_output_elements\030\t\022\033\n\027prefetch_input_elements\030\t\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\200\002\n\033ParallelInterleaveDatasetV2\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\020\n\014cycle_length\030\t\022\020\n\014block_length\030\t\022\026\n\022num_parallel_calls\030\t\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\371\001\n\022ParallelMapDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\022\026\n\022num_parallel_calls\030\003\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"$\n\030use_inter_op_parallelism\022\004bool\032\002(\001\n\314\002\n\023ParseExampleDataset\022\021\n\rinput_dataset\030\025\022\026\n\022num_parallel_calls\030\t\022\030\n\016dense_defaults2\006Tdense\032\n\n\006handle\030\025\"\035\n\013sparse_keys\022\014list(string)(\001\"\034\n\ndense_keys\022\014list(string)(\001\"%\n\014sparse_types\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\037\n\006Tdense\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\035\n\014dense_shapes\022\013list(shape)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\203\001\n\017PrefetchDataset\022\021\n\rinput_dataset\030\025\022\017\n\013buffer_size\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\336\001\n%PrependFromQueueAndPaddedBatchDataset\022\021\n\rinput_dataset\030\025\022\016\n\nbatch_size\030\t\022\024\n\rpadded_shapes\030\t*\001N\022\037\n\016padding_values2\rToutput_types\032\n\n\006handle\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\014\n\001N\022\003int(\0010\001\nu\n\rRandomDataset\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n~\n\014RangeDataset\022\t\n\005start\030\t\022\010\n\004stop\030\t\022\010\n\004step\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\237\002\n\rReduceDataset\022\021\n\rinput_dataset\030\025\022\027\n\rinitial_state2\006Tstate\022\035\n\017other_arguments2\nTarguments\032\032\n\ncomponents2\014output_types\"\t\n\001f\022\004func\"\030\n\006Tstate\022\nlist(type)(\0010\001\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"$\n\030use_inter_op_parallelism\022\004bool\032\002(\001\n{\n\rRepeatDataset\022\021\n\rinput_dataset\030\025\022\t\n\005count\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\347\001\n\013ScanDataset\022\021\n\rinput_dataset\030\025\022\027\n\rinitial_state2\006Tstate\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\030\n\006Tstate\022\nlist(type)(\0010\001\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n;\n\021SerializeIterator\022\023\n\017resource_handle\030\024\032\016\n\nserialized\030\025\210\001\001\n\225\001\n\031SetStatsAggregatorDataset\022\021\n\rinput_dataset\030\025\022\024\n\020stats_aggregator\030\024\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\253\001\n\027ShuffleAndRepeatDataset\022\021\n\rinput_dataset\030\025\022\017\n\013buffer_size\030\t\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\022\t\n\005count\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\275\001\n\016ShuffleDataset\022\021\n\rinput_dataset\030\025\022\017\n\013buffer_size\030\t\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"$\n\030reshuffle_each_iteration\022\004bool\032\002(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n,\n\013SinkDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\ny\n\013SkipDataset\022\021\n\rinput_dataset\030\025\022\t\n\005count\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\245\001\n\014SlideDataset\022\021\n\rinput_dataset\030\025\022\017\n\013window_size\030\t\022\020\n\014window_shift\030\t\022\021\n\rwindow_stride\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\nk\n\030SparseTensorSliceDataset\022\013\n\007indices\030\t\022\021\n\006values\"\007Tvalues\022\017\n\013dense_shape\030\t\032\n\n\006handle\030\025\"\017\n\007Tvalues\022\004type\210\001\001\n\217\001\n\nSqlDataset\022\017\n\013driver_name\030\007\022\024\n\020data_source_name\030\007\022\t\n\005query\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nZ\n\025StatsAggregatorHandle\032\n\n\006handle\030\024\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\n6\n\026StatsAggregatorSummary\022\014\n\010iterator\030\024\032\013\n\007summary\030\007\210\001\001\nV\n\017TFRecordDataset\022\r\n\tfilenames\030\007\022\024\n\020compression_type\030\007\022\017\n\013buffer_size\030\t\032\n\n\006handle\030\025\210\001\001\ny\n\013TakeDataset\022\021\n\rinput_dataset\030\025\022\t\n\005count\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n~\n\rTensorDataset\022\033\n\ncomponents2\rToutput_types\032\n\n\006handle\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\203\001\n\022TensorSliceDataset\022\033\n\ncomponents2\rToutput_types\032\n\n\006handle\030\025\"\037\n\rToutput_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nV\n\017TextLineDataset\022\r\n\tfilenames\030\007\022\024\n\020compression_type\030\007\022\017\n\013buffer_size\030\t\032\n\n\006handle\030\025\210\001\001\nq\n\016UnbatchDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\245\001\n\rWindowDataset\022\021\n\rinput_dataset\030\025\022\010\n\004size\030\t\022\t\n\005shift\030\t\022\n\n\006stride\030\t\022\022\n\016drop_remainder\030\n\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\177\n\nZipDataset\022\025\n\016input_datasets\030\025*\001N\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\014\n\001N\022\003int(\0010\001")
