"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
"""

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 import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls


def all_to_all(input, group_assignment, concat_dimension, split_dimension, split_count, name=None):
  r"""An Op to exchange data across TPU replicas.

  On each replica, the input is split into `split_count` blocks along
  `split_dimension` and send to the other replicas given group_assignment. After
  receiving `split_count` - 1 blocks from other replicas, we concatenate the
  blocks along `concat_dimension` as the output.

  For example, suppose there are 2 TPU replicas:
  replica 0 receives input: `[[A, B]]`
  replica 1 receives input: `[[C, D]]`

  group_assignment=`[[0, 1]]`
  concat_dimension=0
  split_dimension=1
  split_count=2

  replica 0's output: `[[A], [C]]`
  replica 1's output: `[[B], [D]]`

  Args:
    input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
      The local input to the sum.
    group_assignment: A `Tensor` of type `int32`. An int32 tensor with shape
      [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the
      replica ids in the ith subgroup.
    concat_dimension: An `int`. The dimension number to concatenate.
    split_dimension: An `int`. The dimension number to split.
    split_count: An `int`.
      The number of splits, this number must equal to the sub-group
      size(group_assignment.get_shape()[1])
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "AllToAll",
        name, _ctx._post_execution_callbacks, input, group_assignment,
        "concat_dimension", concat_dimension, "split_dimension",
        split_dimension, "split_count", split_count)
      return _result
    except _core._FallbackException:
      try:
        return all_to_all_eager_fallback(
            input, group_assignment, concat_dimension=concat_dimension,
            split_dimension=split_dimension, split_count=split_count,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  concat_dimension = _execute.make_int(concat_dimension, "concat_dimension")
  split_dimension = _execute.make_int(split_dimension, "split_dimension")
  split_count = _execute.make_int(split_count, "split_count")
  _, _, _op = _op_def_lib._apply_op_helper(
        "AllToAll", input=input, group_assignment=group_assignment,
                    concat_dimension=concat_dimension,
                    split_dimension=split_dimension, split_count=split_count,
                    name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "concat_dimension",
            _op.get_attr("concat_dimension"), "split_dimension",
            _op.get_attr("split_dimension"), "split_count",
            _op.get_attr("split_count"))
  _execute.record_gradient(
      "AllToAll", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def AllToAll(input, group_assignment, concat_dimension, split_dimension, split_count, name=None):
  return all_to_all(input=input, group_assignment=group_assignment, concat_dimension=concat_dimension, split_dimension=split_dimension, split_count=split_count, name=name)
AllToAll.__doc__ = all_to_all.__doc__
AllToAll = _doc_controls.do_not_generate_docs(_kwarg_only(AllToAll))
tf_export("raw_ops.AllToAll")(AllToAll)


def all_to_all_eager_fallback(input, group_assignment, concat_dimension, split_dimension, split_count, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function all_to_all
  """
  _ctx = ctx if ctx else _context.context()
  concat_dimension = _execute.make_int(concat_dimension, "concat_dimension")
  split_dimension = _execute.make_int(split_dimension, "split_dimension")
  split_count = _execute.make_int(split_count, "split_count")
  _attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
  group_assignment = _ops.convert_to_tensor(group_assignment, _dtypes.int32)
  _inputs_flat = [input, group_assignment]
  _attrs = ("T", _attr_T, "concat_dimension", concat_dimension,
  "split_dimension", split_dimension, "split_count", split_count)
  _result = _execute.execute(b"AllToAll", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "AllToAll", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def collective_permute(input, source_target_pairs, name=None):
  r"""An Op to permute tensors across replicated TPU instances.

  Each instance supplies its own input.

  For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing
  source_target_pairs=`[[0,1],[1,2],[2,3],[3,0]]` gets the outputs:
  `[D, A, B, C]`.

  Args:
    input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      The local input to be permuted. Currently only supports float and
      bfloat16.
    source_target_pairs: A `Tensor` of type `int32`.
      A tensor with shape [num_pairs, 2].
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "CollectivePermute", name, _ctx._post_execution_callbacks, input,
        source_target_pairs)
      return _result
    except _core._FallbackException:
      try:
        return collective_permute_eager_fallback(
            input, source_target_pairs, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "CollectivePermute", input=input,
                             source_target_pairs=source_target_pairs,
                             name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "CollectivePermute", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def CollectivePermute(input, source_target_pairs, name=None):
  return collective_permute(input=input, source_target_pairs=source_target_pairs, name=name)
CollectivePermute.__doc__ = collective_permute.__doc__
CollectivePermute = _doc_controls.do_not_generate_docs(_kwarg_only(CollectivePermute))
tf_export("raw_ops.CollectivePermute")(CollectivePermute)


def collective_permute_eager_fallback(input, source_target_pairs, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function collective_permute
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
  source_target_pairs = _ops.convert_to_tensor(source_target_pairs, _dtypes.int32)
  _inputs_flat = [input, source_target_pairs]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"CollectivePermute", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "CollectivePermute", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def configure_distributed_tpu(embedding_config="", tpu_embedding_config="", is_global_init=False, name=None):
  r"""Sets up the centralized structures for a distributed TPU system.

  Args:
    embedding_config: An optional `string`. Defaults to `""`.
      Reserved. Do not use.
    tpu_embedding_config: An optional `string`. Defaults to `""`.
      Serialized tensorflow.tpu.TPUEmbeddingConfiguration that
      describes the embedding lookups of the program.
    is_global_init: An optional `bool`. Defaults to `False`.
      Reserved. Do not use.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "ConfigureDistributedTPU", name, _ctx._post_execution_callbacks,
        "embedding_config", embedding_config, "tpu_embedding_config",
        tpu_embedding_config, "is_global_init", is_global_init)
      return _result
    except _core._FallbackException:
      try:
        return configure_distributed_tpu_eager_fallback(
            embedding_config=embedding_config,
            tpu_embedding_config=tpu_embedding_config,
            is_global_init=is_global_init, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if embedding_config is None:
    embedding_config = ""
  embedding_config = _execute.make_str(embedding_config, "embedding_config")
  if tpu_embedding_config is None:
    tpu_embedding_config = ""
  tpu_embedding_config = _execute.make_str(tpu_embedding_config, "tpu_embedding_config")
  if is_global_init is None:
    is_global_init = False
  is_global_init = _execute.make_bool(is_global_init, "is_global_init")
  _, _, _op = _op_def_lib._apply_op_helper(
        "ConfigureDistributedTPU", embedding_config=embedding_config,
                                   tpu_embedding_config=tpu_embedding_config,
                                   is_global_init=is_global_init, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("embedding_config", _op.get_attr("embedding_config"),
            "tpu_embedding_config", _op.get_attr("tpu_embedding_config"),
            "is_global_init", _op.get_attr("is_global_init"))
  _execute.record_gradient(
      "ConfigureDistributedTPU", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def ConfigureDistributedTPU(embedding_config="", tpu_embedding_config="", is_global_init=False, name=None):
  return configure_distributed_tpu(embedding_config=embedding_config, tpu_embedding_config=tpu_embedding_config, is_global_init=is_global_init, name=name)
ConfigureDistributedTPU.__doc__ = configure_distributed_tpu.__doc__
ConfigureDistributedTPU = _doc_controls.do_not_generate_docs(_kwarg_only(ConfigureDistributedTPU))
tf_export("raw_ops.ConfigureDistributedTPU")(ConfigureDistributedTPU)


def configure_distributed_tpu_eager_fallback(embedding_config="", tpu_embedding_config="", is_global_init=False, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function configure_distributed_tpu
  """
  _ctx = ctx if ctx else _context.context()
  if embedding_config is None:
    embedding_config = ""
  embedding_config = _execute.make_str(embedding_config, "embedding_config")
  if tpu_embedding_config is None:
    tpu_embedding_config = ""
  tpu_embedding_config = _execute.make_str(tpu_embedding_config, "tpu_embedding_config")
  if is_global_init is None:
    is_global_init = False
  is_global_init = _execute.make_bool(is_global_init, "is_global_init")
  _inputs_flat = []
  _attrs = ("embedding_config", embedding_config, "tpu_embedding_config",
  tpu_embedding_config, "is_global_init", is_global_init)
  _result = _execute.execute(b"ConfigureDistributedTPU", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "ConfigureDistributedTPU", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def cross_replica_sum(input, group_assignment, name=None):
  r"""An Op to sum inputs across replicated TPU instances.

  Each instance supplies its own input.

  For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`.
  Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0,
  and `B, D, F, H` as group 1. Thus we get the outputs:
  `[A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]`.

  Args:
    input: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `int32`, `uint32`.
      The local input to the sum.
    group_assignment: A `Tensor` of type `int32`. An int32 tensor with shape
      [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the
      replica ids in the ith subgroup.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "CrossReplicaSum", name, _ctx._post_execution_callbacks, input,
        group_assignment)
      return _result
    except _core._FallbackException:
      try:
        return cross_replica_sum_eager_fallback(
            input, group_assignment, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "CrossReplicaSum", input=input, group_assignment=group_assignment,
                           name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "CrossReplicaSum", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def CrossReplicaSum(input, group_assignment, name=None):
  return cross_replica_sum(input=input, group_assignment=group_assignment, name=name)
CrossReplicaSum.__doc__ = cross_replica_sum.__doc__
CrossReplicaSum = _doc_controls.do_not_generate_docs(_kwarg_only(CrossReplicaSum))
tf_export("raw_ops.CrossReplicaSum")(CrossReplicaSum)


def cross_replica_sum_eager_fallback(input, group_assignment, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function cross_replica_sum
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
  group_assignment = _ops.convert_to_tensor(group_assignment, _dtypes.int32)
  _inputs_flat = [input, group_assignment]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"CrossReplicaSum", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "CrossReplicaSum", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def enqueue_tpu_embedding_integer_batch(batch, mode_override, device_ordinal=-1, name=None):
  r"""An op that enqueues a list of input batch tensors to TPUEmbedding.

  Args:
    batch: A list of at least 1 `Tensor` objects with type `int32`.
      A list of 1D tensors, one for each embedding table, containing the
      indices into the tables.
    mode_override: A `Tensor` of type `string`.
      A string input that overrides the mode specified in the
      TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
      'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
      in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. Should be >= 0 and less than the number
      of TPU cores in the task on which the node is placed.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "EnqueueTPUEmbeddingIntegerBatch", name,
        _ctx._post_execution_callbacks, batch, mode_override,
        "device_ordinal", device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return enqueue_tpu_embedding_integer_batch_eager_fallback(
            batch, mode_override, device_ordinal=device_ordinal, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(batch, (list, tuple)):
    raise TypeError(
        "Expected list for 'batch' argument to "
        "'enqueue_tpu_embedding_integer_batch' Op, not %r." % batch)
  _attr_N = len(batch)
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "EnqueueTPUEmbeddingIntegerBatch", batch=batch,
                                           mode_override=mode_override,
                                           device_ordinal=device_ordinal,
                                           name=name)
  return _op
  _result = None
  return _result

def EnqueueTPUEmbeddingIntegerBatch(batch, mode_override, device_ordinal=-1, name=None):
  return enqueue_tpu_embedding_integer_batch(batch=batch, mode_override=mode_override, device_ordinal=device_ordinal, name=name)
EnqueueTPUEmbeddingIntegerBatch.__doc__ = enqueue_tpu_embedding_integer_batch.__doc__
EnqueueTPUEmbeddingIntegerBatch = _doc_controls.do_not_generate_docs(_kwarg_only(EnqueueTPUEmbeddingIntegerBatch))
tf_export("raw_ops.EnqueueTPUEmbeddingIntegerBatch")(EnqueueTPUEmbeddingIntegerBatch)


def enqueue_tpu_embedding_integer_batch_eager_fallback(batch, mode_override, device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function enqueue_tpu_embedding_integer_batch
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(batch, (list, tuple)):
    raise TypeError(
        "Expected list for 'batch' argument to "
        "'enqueue_tpu_embedding_integer_batch' Op, not %r." % batch)
  _attr_N = len(batch)
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  batch = _ops.convert_n_to_tensor(batch, _dtypes.int32)
  mode_override = _ops.convert_to_tensor(mode_override, _dtypes.string)
  _inputs_flat = list(batch) + [mode_override]
  _attrs = ("N", _attr_N, "device_ordinal", device_ordinal)
  _result = _execute.execute(b"EnqueueTPUEmbeddingIntegerBatch", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def enqueue_tpu_embedding_sparse_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, device_ordinal=-1, combiners=[], name=None):
  r"""An op that enqueues TPUEmbedding input indices from a SparseTensor.

  This Op eases the porting of code that uses embedding_lookup_sparse(),
  although some Python preprocessing of the SparseTensor arguments to
  embedding_lookup_sparse() is required to produce the arguments to this Op,
  since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training
  step.

  The tensors at corresponding positions in the three input lists
  must have the same shape, i.e. rank 1 with dim_size() equal to the total
  number of lookups into the table described by the corresponding table_id.

  Args:
    sample_indices: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`.
      A list of rank 1 Tensors specifying the training example and
      feature to which the corresponding embedding_indices and aggregation_weights
      values belong. sample_indices[i] must equal b * nf + f, where nf is the
      number of features from the corresponding table, f is in [0, nf), and
      b is in [0, batch size).
    embedding_indices: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `int32`, `int64`.
      A list of rank 1 Tensors, indices into the embedding tables.
    aggregation_weights: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `float32`, `float64`.
      A list of rank 1 Tensors containing per sample -- i.e. per
      (training example, feature) -- aggregation weights.
    mode_override: A `Tensor` of type `string`.
      A string input that overrides the mode specified in the
      TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
      'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
      in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. Should be >= 0 and less than the number
      of TPU cores in the task on which the node is placed.
    combiners: An optional list of `strings`. Defaults to `[]`.
      A list of string scalars, one for each embedding table that specify
      how to normalize the embedding activations after weighted summation.
      Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have
      the sum of the weights be 0 for 'mean' or the sum of the squared weights be
      0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for
      all tables.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "EnqueueTPUEmbeddingSparseBatch", name,
        _ctx._post_execution_callbacks, sample_indices, embedding_indices,
        aggregation_weights, mode_override, "device_ordinal", device_ordinal,
        "combiners", combiners)
      return _result
    except _core._FallbackException:
      try:
        return enqueue_tpu_embedding_sparse_batch_eager_fallback(
            sample_indices, embedding_indices, aggregation_weights,
            mode_override, device_ordinal=device_ordinal, combiners=combiners,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(sample_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'sample_indices' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % sample_indices)
  _attr_N = len(sample_indices)
  if not isinstance(embedding_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'embedding_indices' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % embedding_indices)
  if len(embedding_indices) != _attr_N:
    raise ValueError(
        "List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(embedding_indices), _attr_N))
  if not isinstance(aggregation_weights, (list, tuple)):
    raise TypeError(
        "Expected list for 'aggregation_weights' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % aggregation_weights)
  if len(aggregation_weights) != _attr_N:
    raise ValueError(
        "List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(aggregation_weights), _attr_N))
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  if combiners is None:
    combiners = []
  if not isinstance(combiners, (list, tuple)):
    raise TypeError(
        "Expected list for 'combiners' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % combiners)
  combiners = [_execute.make_str(_s, "combiners") for _s in combiners]
  _, _, _op = _op_def_lib._apply_op_helper(
        "EnqueueTPUEmbeddingSparseBatch", sample_indices=sample_indices,
                                          embedding_indices=embedding_indices,
                                          aggregation_weights=aggregation_weights,
                                          mode_override=mode_override,
                                          device_ordinal=device_ordinal,
                                          combiners=combiners, name=name)
  return _op
  _result = None
  return _result

def EnqueueTPUEmbeddingSparseBatch(sample_indices, embedding_indices, aggregation_weights, mode_override, device_ordinal=-1, combiners=[], name=None):
  return enqueue_tpu_embedding_sparse_batch(sample_indices=sample_indices, embedding_indices=embedding_indices, aggregation_weights=aggregation_weights, mode_override=mode_override, device_ordinal=device_ordinal, combiners=combiners, name=name)
EnqueueTPUEmbeddingSparseBatch.__doc__ = enqueue_tpu_embedding_sparse_batch.__doc__
EnqueueTPUEmbeddingSparseBatch = _doc_controls.do_not_generate_docs(_kwarg_only(EnqueueTPUEmbeddingSparseBatch))
tf_export("raw_ops.EnqueueTPUEmbeddingSparseBatch")(EnqueueTPUEmbeddingSparseBatch)


def enqueue_tpu_embedding_sparse_batch_eager_fallback(sample_indices, embedding_indices, aggregation_weights, mode_override, device_ordinal=-1, combiners=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function enqueue_tpu_embedding_sparse_batch
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(sample_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'sample_indices' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % sample_indices)
  _attr_N = len(sample_indices)
  if not isinstance(embedding_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'embedding_indices' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % embedding_indices)
  if len(embedding_indices) != _attr_N:
    raise ValueError(
        "List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(embedding_indices), _attr_N))
  if not isinstance(aggregation_weights, (list, tuple)):
    raise TypeError(
        "Expected list for 'aggregation_weights' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % aggregation_weights)
  if len(aggregation_weights) != _attr_N:
    raise ValueError(
        "List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(aggregation_weights), _attr_N))
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  if combiners is None:
    combiners = []
  if not isinstance(combiners, (list, tuple)):
    raise TypeError(
        "Expected list for 'combiners' argument to "
        "'enqueue_tpu_embedding_sparse_batch' Op, not %r." % combiners)
  combiners = [_execute.make_str(_s, "combiners") for _s in combiners]
  _attr_T1, sample_indices = _execute.args_to_matching_eager(list(sample_indices), _ctx, _dtypes.int32)
  _attr_T2, embedding_indices = _execute.args_to_matching_eager(list(embedding_indices), _ctx, _dtypes.int32)
  _attr_T3, aggregation_weights = _execute.args_to_matching_eager(list(aggregation_weights), _ctx, _dtypes.float32)
  mode_override = _ops.convert_to_tensor(mode_override, _dtypes.string)
  _inputs_flat = list(sample_indices) + list(embedding_indices) + list(aggregation_weights) + [mode_override]
  _attrs = ("T1", _attr_T1, "T2", _attr_T2, "T3", _attr_T3, "N", _attr_N,
  "device_ordinal", device_ordinal, "combiners", combiners)
  _result = _execute.execute(b"EnqueueTPUEmbeddingSparseBatch", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def enqueue_tpu_embedding_sparse_tensor_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, table_ids, device_ordinal=-1, combiners=[], max_sequence_lengths=[], name=None):
  r"""Eases the porting of code that uses tf.nn.embedding_lookup_sparse().

  sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond
  to the ith feature. table_ids[i] indicates which embedding table to look up ith
  feature.

  The tensors at corresponding positions in the three input lists (sample_indices,
  embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1
  with dim_size() equal to the total number of lookups into the table described by
  the corresponding feature.

  Args:
    sample_indices: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`.
      A list of rank 1 Tensors specifying the training example to
      which the corresponding embedding_indices and aggregation_weights values
      belong. It corresponds to sp_ids.indices[:,0] in  embedding_lookup_sparse().
    embedding_indices: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `int32`, `int64`.
      A list of rank 1 Tensors, indices into the embedding tables.
      It corresponds to sp_ids.values in embedding_lookup_sparse().
    aggregation_weights: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `float32`, `float64`.
      A list of rank 1 Tensors containing per training example
      aggregation weights. It corresponds to sp_weights.values in
      embedding_lookup_sparse().
    mode_override: A `Tensor` of type `string`.
      A string input that overrides the mode specified in the
      TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
      'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
      in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
    table_ids: A list of `ints`.
      A list of integers specifying the identifier of the embedding table
      (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the
      corresponding input. The ith input is looked up using table_ids[i]. The size
      of the table_ids list must be equal to that of sample_indices,
      embedding_indices and aggregation_weights.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. Should be >= 0 and less than the number
      of TPU cores in the task on which the node is placed.
    combiners: An optional list of `strings`. Defaults to `[]`.
      A list of string scalars, one for each embedding table that specify
      how to normalize the embedding activations after weighted summation.
      Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have
      the sum of the weights be 0 for 'mean' or the sum of the squared weights be
      0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for
      all tables.
    max_sequence_lengths: An optional list of `ints`. Defaults to `[]`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "EnqueueTPUEmbeddingSparseTensorBatch", name,
        _ctx._post_execution_callbacks, sample_indices, embedding_indices,
        aggregation_weights, mode_override, "device_ordinal", device_ordinal,
        "combiners", combiners, "table_ids", table_ids,
        "max_sequence_lengths", max_sequence_lengths)
      return _result
    except _core._FallbackException:
      try:
        return enqueue_tpu_embedding_sparse_tensor_batch_eager_fallback(
            sample_indices, embedding_indices, aggregation_weights,
            mode_override, device_ordinal=device_ordinal, combiners=combiners,
            table_ids=table_ids, max_sequence_lengths=max_sequence_lengths,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(sample_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'sample_indices' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % sample_indices)
  _attr_N = len(sample_indices)
  if not isinstance(embedding_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'embedding_indices' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % embedding_indices)
  if len(embedding_indices) != _attr_N:
    raise ValueError(
        "List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(embedding_indices), _attr_N))
  if not isinstance(aggregation_weights, (list, tuple)):
    raise TypeError(
        "Expected list for 'aggregation_weights' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % aggregation_weights)
  if len(aggregation_weights) != _attr_N:
    raise ValueError(
        "List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(aggregation_weights), _attr_N))
  if not isinstance(table_ids, (list, tuple)):
    raise TypeError(
        "Expected list for 'table_ids' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % table_ids)
  table_ids = [_execute.make_int(_i, "table_ids") for _i in table_ids]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  if combiners is None:
    combiners = []
  if not isinstance(combiners, (list, tuple)):
    raise TypeError(
        "Expected list for 'combiners' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % combiners)
  combiners = [_execute.make_str(_s, "combiners") for _s in combiners]
  if max_sequence_lengths is None:
    max_sequence_lengths = []
  if not isinstance(max_sequence_lengths, (list, tuple)):
    raise TypeError(
        "Expected list for 'max_sequence_lengths' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % max_sequence_lengths)
  max_sequence_lengths = [_execute.make_int(_i, "max_sequence_lengths") for _i in max_sequence_lengths]
  _, _, _op = _op_def_lib._apply_op_helper(
        "EnqueueTPUEmbeddingSparseTensorBatch", sample_indices=sample_indices,
                                                embedding_indices=embedding_indices,
                                                aggregation_weights=aggregation_weights,
                                                mode_override=mode_override,
                                                table_ids=table_ids,
                                                device_ordinal=device_ordinal,
                                                combiners=combiners,
                                                max_sequence_lengths=max_sequence_lengths,
                                                name=name)
  return _op
  _result = None
  return _result

def EnqueueTPUEmbeddingSparseTensorBatch(sample_indices, embedding_indices, aggregation_weights, mode_override, table_ids, device_ordinal=-1, combiners=[], max_sequence_lengths=[], name=None):
  return enqueue_tpu_embedding_sparse_tensor_batch(sample_indices=sample_indices, embedding_indices=embedding_indices, aggregation_weights=aggregation_weights, mode_override=mode_override, table_ids=table_ids, device_ordinal=device_ordinal, combiners=combiners, max_sequence_lengths=max_sequence_lengths, name=name)
EnqueueTPUEmbeddingSparseTensorBatch.__doc__ = enqueue_tpu_embedding_sparse_tensor_batch.__doc__
EnqueueTPUEmbeddingSparseTensorBatch = _doc_controls.do_not_generate_docs(_kwarg_only(EnqueueTPUEmbeddingSparseTensorBatch))
tf_export("raw_ops.EnqueueTPUEmbeddingSparseTensorBatch")(EnqueueTPUEmbeddingSparseTensorBatch)


def enqueue_tpu_embedding_sparse_tensor_batch_eager_fallback(sample_indices, embedding_indices, aggregation_weights, mode_override, table_ids, device_ordinal=-1, combiners=[], max_sequence_lengths=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function enqueue_tpu_embedding_sparse_tensor_batch
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(sample_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'sample_indices' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % sample_indices)
  _attr_N = len(sample_indices)
  if not isinstance(embedding_indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'embedding_indices' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % embedding_indices)
  if len(embedding_indices) != _attr_N:
    raise ValueError(
        "List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(embedding_indices), _attr_N))
  if not isinstance(aggregation_weights, (list, tuple)):
    raise TypeError(
        "Expected list for 'aggregation_weights' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % aggregation_weights)
  if len(aggregation_weights) != _attr_N:
    raise ValueError(
        "List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d "
        "must match length %d of argument 'sample_indices'." %
        (len(aggregation_weights), _attr_N))
  if not isinstance(table_ids, (list, tuple)):
    raise TypeError(
        "Expected list for 'table_ids' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % table_ids)
  table_ids = [_execute.make_int(_i, "table_ids") for _i in table_ids]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  if combiners is None:
    combiners = []
  if not isinstance(combiners, (list, tuple)):
    raise TypeError(
        "Expected list for 'combiners' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % combiners)
  combiners = [_execute.make_str(_s, "combiners") for _s in combiners]
  if max_sequence_lengths is None:
    max_sequence_lengths = []
  if not isinstance(max_sequence_lengths, (list, tuple)):
    raise TypeError(
        "Expected list for 'max_sequence_lengths' argument to "
        "'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r." % max_sequence_lengths)
  max_sequence_lengths = [_execute.make_int(_i, "max_sequence_lengths") for _i in max_sequence_lengths]
  _attr_T1, sample_indices = _execute.args_to_matching_eager(list(sample_indices), _ctx, _dtypes.int32)
  _attr_T2, embedding_indices = _execute.args_to_matching_eager(list(embedding_indices), _ctx, _dtypes.int32)
  _attr_T3, aggregation_weights = _execute.args_to_matching_eager(list(aggregation_weights), _ctx, _dtypes.float32)
  mode_override = _ops.convert_to_tensor(mode_override, _dtypes.string)
  _inputs_flat = list(sample_indices) + list(embedding_indices) + list(aggregation_weights) + [mode_override]
  _attrs = ("T1", _attr_T1, "T2", _attr_T2, "T3", _attr_T3, "N", _attr_N,
  "device_ordinal", device_ordinal, "combiners", combiners, "table_ids",
  table_ids, "max_sequence_lengths", max_sequence_lengths)
  _result = _execute.execute(b"EnqueueTPUEmbeddingSparseTensorBatch", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def infeed_dequeue(dtype, shape, name=None):
  r"""A placeholder op for a value that will be fed into the computation.

  Args:
    dtype: A `tf.DType`. The type of elements in the tensor.
    shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "InfeedDequeue", name, _ctx._post_execution_callbacks, "dtype", dtype,
        "shape", shape)
      return _result
    except _core._FallbackException:
      try:
        return infeed_dequeue_eager_fallback(
            dtype=dtype, shape=shape, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  dtype = _execute.make_type(dtype, "dtype")
  shape = _execute.make_shape(shape, "shape")
  _, _, _op = _op_def_lib._apply_op_helper(
        "InfeedDequeue", dtype=dtype, shape=shape, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"))
  _execute.record_gradient(
      "InfeedDequeue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def InfeedDequeue(dtype, shape, name=None):
  return infeed_dequeue(dtype=dtype, shape=shape, name=name)
InfeedDequeue.__doc__ = infeed_dequeue.__doc__
InfeedDequeue = _doc_controls.do_not_generate_docs(_kwarg_only(InfeedDequeue))
tf_export("raw_ops.InfeedDequeue")(InfeedDequeue)


def infeed_dequeue_eager_fallback(dtype, shape, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function infeed_dequeue
  """
  _ctx = ctx if ctx else _context.context()
  dtype = _execute.make_type(dtype, "dtype")
  shape = _execute.make_shape(shape, "shape")
  _inputs_flat = []
  _attrs = ("dtype", dtype, "shape", shape)
  _result = _execute.execute(b"InfeedDequeue", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "InfeedDequeue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def infeed_dequeue_tuple(dtypes, shapes, name=None):
  r"""Fetches multiple values from infeed as an XLA tuple.

  Args:
    dtypes: A list of `tf.DTypes` that has length `>= 1`.
      The element types of each element in `outputs`.
    shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
      The shapes of each tensor in `outputs`.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `dtypes`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "InfeedDequeueTuple", name, _ctx._post_execution_callbacks, "dtypes",
        dtypes, "shapes", shapes)
      return _result
    except _core._FallbackException:
      try:
        return infeed_dequeue_tuple_eager_fallback(
            dtypes=dtypes, shapes=shapes, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(dtypes, (list, tuple)):
    raise TypeError(
        "Expected list for 'dtypes' argument to "
        "'infeed_dequeue_tuple' Op, not %r." % dtypes)
  dtypes = [_execute.make_type(_t, "dtypes") for _t in dtypes]
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'infeed_dequeue_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  _, _, _op = _op_def_lib._apply_op_helper(
        "InfeedDequeueTuple", dtypes=dtypes, shapes=shapes, name=name)
  _result = _op.outputs[:]
  if not _result:
    return _op
  _inputs_flat = _op.inputs
  _attrs = ("dtypes", _op.get_attr("dtypes"), "shapes",
            _op.get_attr("shapes"))
  _execute.record_gradient(
      "InfeedDequeueTuple", _inputs_flat, _attrs, _result, name)
  return _result

def InfeedDequeueTuple(dtypes, shapes, name=None):
  return infeed_dequeue_tuple(dtypes=dtypes, shapes=shapes, name=name)
InfeedDequeueTuple.__doc__ = infeed_dequeue_tuple.__doc__
InfeedDequeueTuple = _doc_controls.do_not_generate_docs(_kwarg_only(InfeedDequeueTuple))
tf_export("raw_ops.InfeedDequeueTuple")(InfeedDequeueTuple)


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


def infeed_enqueue(input, shape=[], layout=[], device_ordinal=-1, name=None):
  r"""An op which feeds a single Tensor value into the computation.

  Args:
    input: A `Tensor`.
      A tensor that will be provided using the infeed mechanism.
    shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
      The shape of the tensor.
    layout: An optional list of `ints`. Defaults to `[]`.
      A vector holding the requested layout in minor-to-major sequence.
      If a layout attribute is passed, but its values are all -1, the layout will
      be computed by the infeed operation.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. This should be -1 when the Op
      is running on a TPU device, and >= 0 when the Op is running on the CPU
      device.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "InfeedEnqueue", name, _ctx._post_execution_callbacks, input, "shape",
        shape, "layout", layout, "device_ordinal", device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return infeed_enqueue_eager_fallback(
            input, shape=shape, layout=layout, device_ordinal=device_ordinal,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if shape is None:
    shape = []
  shape = _execute.make_shape(shape, "shape")
  if layout is None:
    layout = []
  if not isinstance(layout, (list, tuple)):
    raise TypeError(
        "Expected list for 'layout' argument to "
        "'infeed_enqueue' Op, not %r." % layout)
  layout = [_execute.make_int(_i, "layout") for _i in layout]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "InfeedEnqueue", input=input, shape=shape, layout=layout,
                         device_ordinal=device_ordinal, name=name)
  return _op
  _result = None
  return _result

def InfeedEnqueue(input, shape=[], layout=[], device_ordinal=-1, name=None):
  return infeed_enqueue(input=input, shape=shape, layout=layout, device_ordinal=device_ordinal, name=name)
InfeedEnqueue.__doc__ = infeed_enqueue.__doc__
InfeedEnqueue = _doc_controls.do_not_generate_docs(_kwarg_only(InfeedEnqueue))
tf_export("raw_ops.InfeedEnqueue")(InfeedEnqueue)


def infeed_enqueue_eager_fallback(input, shape=[], layout=[], device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function infeed_enqueue
  """
  _ctx = ctx if ctx else _context.context()
  if shape is None:
    shape = []
  shape = _execute.make_shape(shape, "shape")
  if layout is None:
    layout = []
  if not isinstance(layout, (list, tuple)):
    raise TypeError(
        "Expected list for 'layout' argument to "
        "'infeed_enqueue' Op, not %r." % layout)
  layout = [_execute.make_int(_i, "layout") for _i in layout]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _attr_dtype, (input,) = _execute.args_to_matching_eager([input], _ctx)
  _inputs_flat = [input]
  _attrs = ("dtype", _attr_dtype, "shape", shape, "layout", layout,
  "device_ordinal", device_ordinal)
  _result = _execute.execute(b"InfeedEnqueue", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def infeed_enqueue_prelinearized_buffer(input, device_ordinal=-1, name=None):
  r"""An op which enqueues prelinearized buffer into TPU infeed.

  Args:
    input: A `Tensor` of type `variant`.
      A variant tensor representing linearized output.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. This should be -1 when the Op is running on a TPU device
      and = 0 when the Op is running on the CPU device.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "InfeedEnqueuePrelinearizedBuffer", name,
        _ctx._post_execution_callbacks, input, "device_ordinal",
        device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return infeed_enqueue_prelinearized_buffer_eager_fallback(
            input, device_ordinal=device_ordinal, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "InfeedEnqueuePrelinearizedBuffer", input=input,
                                            device_ordinal=device_ordinal,
                                            name=name)
  return _op
  _result = None
  return _result

def InfeedEnqueuePrelinearizedBuffer(input, device_ordinal=-1, name=None):
  return infeed_enqueue_prelinearized_buffer(input=input, device_ordinal=device_ordinal, name=name)
InfeedEnqueuePrelinearizedBuffer.__doc__ = infeed_enqueue_prelinearized_buffer.__doc__
InfeedEnqueuePrelinearizedBuffer = _doc_controls.do_not_generate_docs(_kwarg_only(InfeedEnqueuePrelinearizedBuffer))
tf_export("raw_ops.InfeedEnqueuePrelinearizedBuffer")(InfeedEnqueuePrelinearizedBuffer)


def infeed_enqueue_prelinearized_buffer_eager_fallback(input, device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function infeed_enqueue_prelinearized_buffer
  """
  _ctx = ctx if ctx else _context.context()
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  input = _ops.convert_to_tensor(input, _dtypes.variant)
  _inputs_flat = [input]
  _attrs = ("device_ordinal", device_ordinal)
  _result = _execute.execute(b"InfeedEnqueuePrelinearizedBuffer", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def infeed_enqueue_tuple(inputs, shapes, layouts=[], device_ordinal=-1, name=None):
  r"""Feeds multiple Tensor values into the computation as an XLA tuple.

  Args:
    inputs: A list of `Tensor` objects.
      A list of tensors that will be provided using the infeed mechanism.
    shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
      The shapes of each tensor in `inputs`.
    layouts: An optional list of `ints`. Defaults to `[]`.
      A vector holding the requested layout in minor-to-major sequence for
      all the tuple shapes, in the order the shapes appear in the "shapes" input.
      The layout elements for a sub-shape can be set to -1, in which case the
      corresponding layout will be computed by the infeed operation.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. This should be -1 when the Op
      is running on a TPU device, and >= 0 when the Op is running on the CPU
      device.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "InfeedEnqueueTuple", name, _ctx._post_execution_callbacks, inputs,
        "shapes", shapes, "layouts", layouts, "device_ordinal",
        device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return infeed_enqueue_tuple_eager_fallback(
            inputs, shapes=shapes, layouts=layouts,
            device_ordinal=device_ordinal, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'infeed_enqueue_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if layouts is None:
    layouts = []
  if not isinstance(layouts, (list, tuple)):
    raise TypeError(
        "Expected list for 'layouts' argument to "
        "'infeed_enqueue_tuple' Op, not %r." % layouts)
  layouts = [_execute.make_int(_i, "layouts") for _i in layouts]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "InfeedEnqueueTuple", inputs=inputs, shapes=shapes, layouts=layouts,
                              device_ordinal=device_ordinal, name=name)
  return _op
  _result = None
  return _result

def InfeedEnqueueTuple(inputs, shapes, layouts=[], device_ordinal=-1, name=None):
  return infeed_enqueue_tuple(inputs=inputs, shapes=shapes, layouts=layouts, device_ordinal=device_ordinal, name=name)
InfeedEnqueueTuple.__doc__ = infeed_enqueue_tuple.__doc__
InfeedEnqueueTuple = _doc_controls.do_not_generate_docs(_kwarg_only(InfeedEnqueueTuple))
tf_export("raw_ops.InfeedEnqueueTuple")(InfeedEnqueueTuple)


def infeed_enqueue_tuple_eager_fallback(inputs, shapes, layouts=[], device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function infeed_enqueue_tuple
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'infeed_enqueue_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if layouts is None:
    layouts = []
  if not isinstance(layouts, (list, tuple)):
    raise TypeError(
        "Expected list for 'layouts' argument to "
        "'infeed_enqueue_tuple' Op, not %r." % layouts)
  layouts = [_execute.make_int(_i, "layouts") for _i in layouts]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _attr_dtypes, inputs = _execute.convert_to_mixed_eager_tensors(inputs, _ctx)
  _inputs_flat = list(inputs)
  _attrs = ("dtypes", _attr_dtypes, "shapes", shapes, "layouts", layouts,
  "device_ordinal", device_ordinal)
  _result = _execute.execute(b"InfeedEnqueueTuple", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def load_tpu_embedding_adam_parameters(parameters, momenta, velocities, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load ADAM embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the ADAM optimization algorithm.
    momenta: A `Tensor` of type `float32`.
      Value of momenta used in the ADAM optimization algorithm.
    velocities: A `Tensor` of type `float32`.
      Value of velocities used in the ADAM optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingADAMParameters", name,
        _ctx._post_execution_callbacks, parameters, momenta, velocities,
        "table_id", table_id, "table_name", table_name, "num_shards",
        num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adam_parameters_eager_fallback(
            parameters, momenta, velocities, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingADAMParameters", parameters=parameters,
                                          momenta=momenta,
                                          velocities=velocities,
                                          num_shards=num_shards,
                                          shard_id=shard_id,
                                          table_id=table_id,
                                          table_name=table_name, name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingADAMParameters(parameters, momenta, velocities, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adam_parameters(parameters=parameters, momenta=momenta, velocities=velocities, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingADAMParameters.__doc__ = load_tpu_embedding_adam_parameters.__doc__
LoadTPUEmbeddingADAMParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingADAMParameters))
tf_export("raw_ops.LoadTPUEmbeddingADAMParameters")(LoadTPUEmbeddingADAMParameters)


def load_tpu_embedding_adam_parameters_eager_fallback(parameters, momenta, velocities, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adam_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  momenta = _ops.convert_to_tensor(momenta, _dtypes.float32)
  velocities = _ops.convert_to_tensor(velocities, _dtypes.float32)
  _inputs_flat = [parameters, momenta, velocities]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingADAMParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_adam_parameters_grad_accum_debug(parameters, momenta, velocities, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load ADAM embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the ADAM optimization algorithm.
    momenta: A `Tensor` of type `float32`.
      Value of momenta used in the ADAM optimization algorithm.
    velocities: A `Tensor` of type `float32`.
      Value of velocities used in the ADAM optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the ADAM optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingADAMParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, momenta, velocities,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adam_parameters_grad_accum_debug_eager_fallback(
            parameters, momenta, velocities, gradient_accumulators,
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingADAMParametersGradAccumDebug", parameters=parameters,
                                                        momenta=momenta,
                                                        velocities=velocities,
                                                        gradient_accumulators=gradient_accumulators,
                                                        num_shards=num_shards,
                                                        shard_id=shard_id,
                                                        table_id=table_id,
                                                        table_name=table_name,
                                                        name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingADAMParametersGradAccumDebug(parameters, momenta, velocities, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adam_parameters_grad_accum_debug(parameters=parameters, momenta=momenta, velocities=velocities, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingADAMParametersGradAccumDebug.__doc__ = load_tpu_embedding_adam_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingADAMParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingADAMParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingADAMParametersGradAccumDebug")(LoadTPUEmbeddingADAMParametersGradAccumDebug)


def load_tpu_embedding_adam_parameters_grad_accum_debug_eager_fallback(parameters, momenta, velocities, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adam_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  momenta = _ops.convert_to_tensor(momenta, _dtypes.float32)
  velocities = _ops.convert_to_tensor(velocities, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, momenta, velocities, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingADAMParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_adadelta_parameters(parameters, accumulators, updates, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Adadelta embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Adadelta optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the Adadelta optimization algorithm.
    updates: A `Tensor` of type `float32`.
      Value of updates used in the Adadelta optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingAdadeltaParameters", name,
        _ctx._post_execution_callbacks, parameters, accumulators, updates,
        "table_id", table_id, "table_name", table_name, "num_shards",
        num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adadelta_parameters_eager_fallback(
            parameters, accumulators, updates, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingAdadeltaParameters", parameters=parameters,
                                              accumulators=accumulators,
                                              updates=updates,
                                              num_shards=num_shards,
                                              shard_id=shard_id,
                                              table_id=table_id,
                                              table_name=table_name,
                                              name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingAdadeltaParameters(parameters, accumulators, updates, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adadelta_parameters(parameters=parameters, accumulators=accumulators, updates=updates, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingAdadeltaParameters.__doc__ = load_tpu_embedding_adadelta_parameters.__doc__
LoadTPUEmbeddingAdadeltaParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingAdadeltaParameters))
tf_export("raw_ops.LoadTPUEmbeddingAdadeltaParameters")(LoadTPUEmbeddingAdadeltaParameters)


def load_tpu_embedding_adadelta_parameters_eager_fallback(parameters, accumulators, updates, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adadelta_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  updates = _ops.convert_to_tensor(updates, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, updates]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingAdadeltaParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_adadelta_parameters_grad_accum_debug(parameters, accumulators, updates, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Adadelta parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Adadelta optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the Adadelta optimization algorithm.
    updates: A `Tensor` of type `float32`.
      Value of updates used in the Adadelta optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the Adadelta optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, accumulators, updates,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adadelta_parameters_grad_accum_debug_eager_fallback(
            parameters, accumulators, updates, gradient_accumulators,
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", parameters=parameters,
                                                            accumulators=accumulators,
                                                            updates=updates,
                                                            gradient_accumulators=gradient_accumulators,
                                                            num_shards=num_shards,
                                                            shard_id=shard_id,
                                                            table_id=table_id,
                                                            table_name=table_name,
                                                            name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(parameters, accumulators, updates, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adadelta_parameters_grad_accum_debug(parameters=parameters, accumulators=accumulators, updates=updates, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingAdadeltaParametersGradAccumDebug.__doc__ = load_tpu_embedding_adadelta_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingAdadeltaParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingAdadeltaParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingAdadeltaParametersGradAccumDebug")(LoadTPUEmbeddingAdadeltaParametersGradAccumDebug)


def load_tpu_embedding_adadelta_parameters_grad_accum_debug_eager_fallback(parameters, accumulators, updates, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adadelta_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  updates = _ops.convert_to_tensor(updates, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, updates, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingAdadeltaParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_adagrad_parameters(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Adagrad embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Adagrad optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the Adagrad optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingAdagradParameters", name,
        _ctx._post_execution_callbacks, parameters, accumulators, "table_id",
        table_id, "table_name", table_name, "num_shards", num_shards,
        "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adagrad_parameters_eager_fallback(
            parameters, accumulators, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingAdagradParameters", parameters=parameters,
                                             accumulators=accumulators,
                                             num_shards=num_shards,
                                             shard_id=shard_id,
                                             table_id=table_id,
                                             table_name=table_name, name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingAdagradParameters(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adagrad_parameters(parameters=parameters, accumulators=accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingAdagradParameters.__doc__ = load_tpu_embedding_adagrad_parameters.__doc__
LoadTPUEmbeddingAdagradParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingAdagradParameters))
tf_export("raw_ops.LoadTPUEmbeddingAdagradParameters")(LoadTPUEmbeddingAdagradParameters)


def load_tpu_embedding_adagrad_parameters_eager_fallback(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adagrad_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingAdagradParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Adagrad embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Adagrad optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the Adagrad optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the Adagrad optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingAdagradParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, accumulators,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_adagrad_parameters_grad_accum_debug_eager_fallback(
            parameters, accumulators, gradient_accumulators,
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingAdagradParametersGradAccumDebug", parameters=parameters,
                                                           accumulators=accumulators,
                                                           gradient_accumulators=gradient_accumulators,
                                                           num_shards=num_shards,
                                                           shard_id=shard_id,
                                                           table_id=table_id,
                                                           table_name=table_name,
                                                           name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingAdagradParametersGradAccumDebug(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_adagrad_parameters_grad_accum_debug(parameters=parameters, accumulators=accumulators, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingAdagradParametersGradAccumDebug.__doc__ = load_tpu_embedding_adagrad_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingAdagradParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingAdagradParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingAdagradParametersGradAccumDebug")(LoadTPUEmbeddingAdagradParametersGradAccumDebug)


def load_tpu_embedding_adagrad_parameters_grad_accum_debug_eager_fallback(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_adagrad_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingAdagradParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_centered_rms_prop_parameters(parameters, ms, mom, mg, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load centered RMSProp embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the centered RMSProp optimization algorithm.
    ms: A `Tensor` of type `float32`.
      Value of ms used in the centered RMSProp optimization algorithm.
    mom: A `Tensor` of type `float32`.
      Value of mom used in the centered RMSProp optimization algorithm.
    mg: A `Tensor` of type `float32`.
      Value of mg used in the centered RMSProp optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingCenteredRMSPropParameters", name,
        _ctx._post_execution_callbacks, parameters, ms, mom, mg, "table_id",
        table_id, "table_name", table_name, "num_shards", num_shards,
        "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_centered_rms_prop_parameters_eager_fallback(
            parameters, ms, mom, mg, table_id=table_id, table_name=table_name,
            num_shards=num_shards, shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingCenteredRMSPropParameters", parameters=parameters,
                                                     ms=ms, mom=mom, mg=mg,
                                                     num_shards=num_shards,
                                                     shard_id=shard_id,
                                                     table_id=table_id,
                                                     table_name=table_name,
                                                     name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingCenteredRMSPropParameters(parameters, ms, mom, mg, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_centered_rms_prop_parameters(parameters=parameters, ms=ms, mom=mom, mg=mg, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingCenteredRMSPropParameters.__doc__ = load_tpu_embedding_centered_rms_prop_parameters.__doc__
LoadTPUEmbeddingCenteredRMSPropParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingCenteredRMSPropParameters))
tf_export("raw_ops.LoadTPUEmbeddingCenteredRMSPropParameters")(LoadTPUEmbeddingCenteredRMSPropParameters)


def load_tpu_embedding_centered_rms_prop_parameters_eager_fallback(parameters, ms, mom, mg, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_centered_rms_prop_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  ms = _ops.convert_to_tensor(ms, _dtypes.float32)
  mom = _ops.convert_to_tensor(mom, _dtypes.float32)
  mg = _ops.convert_to_tensor(mg, _dtypes.float32)
  _inputs_flat = [parameters, ms, mom, mg]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingCenteredRMSPropParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_ftrl_parameters(parameters, accumulators, linears, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load FTRL embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the FTRL optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the FTRL optimization algorithm.
    linears: A `Tensor` of type `float32`.
      Value of linears used in the FTRL optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingFTRLParameters", name,
        _ctx._post_execution_callbacks, parameters, accumulators, linears,
        "table_id", table_id, "table_name", table_name, "num_shards",
        num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_ftrl_parameters_eager_fallback(
            parameters, accumulators, linears, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingFTRLParameters", parameters=parameters,
                                          accumulators=accumulators,
                                          linears=linears,
                                          num_shards=num_shards,
                                          shard_id=shard_id,
                                          table_id=table_id,
                                          table_name=table_name, name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingFTRLParameters(parameters, accumulators, linears, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_ftrl_parameters(parameters=parameters, accumulators=accumulators, linears=linears, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingFTRLParameters.__doc__ = load_tpu_embedding_ftrl_parameters.__doc__
LoadTPUEmbeddingFTRLParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingFTRLParameters))
tf_export("raw_ops.LoadTPUEmbeddingFTRLParameters")(LoadTPUEmbeddingFTRLParameters)


def load_tpu_embedding_ftrl_parameters_eager_fallback(parameters, accumulators, linears, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_ftrl_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  linears = _ops.convert_to_tensor(linears, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, linears]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingFTRLParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_ftrl_parameters_grad_accum_debug(parameters, accumulators, linears, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load FTRL embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the FTRL optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the FTRL optimization algorithm.
    linears: A `Tensor` of type `float32`.
      Value of linears used in the FTRL optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the FTRL optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingFTRLParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, accumulators, linears,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_ftrl_parameters_grad_accum_debug_eager_fallback(
            parameters, accumulators, linears, gradient_accumulators,
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingFTRLParametersGradAccumDebug", parameters=parameters,
                                                        accumulators=accumulators,
                                                        linears=linears,
                                                        gradient_accumulators=gradient_accumulators,
                                                        num_shards=num_shards,
                                                        shard_id=shard_id,
                                                        table_id=table_id,
                                                        table_name=table_name,
                                                        name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingFTRLParametersGradAccumDebug(parameters, accumulators, linears, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_ftrl_parameters_grad_accum_debug(parameters=parameters, accumulators=accumulators, linears=linears, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingFTRLParametersGradAccumDebug.__doc__ = load_tpu_embedding_ftrl_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingFTRLParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingFTRLParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug")(LoadTPUEmbeddingFTRLParametersGradAccumDebug)


def load_tpu_embedding_ftrl_parameters_grad_accum_debug_eager_fallback(parameters, accumulators, linears, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_ftrl_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  linears = _ops.convert_to_tensor(linears, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, linears, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingFTRLParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_mdl_adagrad_light_parameters(parameters, accumulators, weights, benefits, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load MDL Adagrad Light embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the MDL Adagrad Light optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the MDL Adagrad Light optimization algorithm.
    weights: A `Tensor` of type `float32`.
      Value of weights used in the MDL Adagrad Light optimization algorithm.
    benefits: A `Tensor` of type `float32`.
      Value of benefits used in the MDL Adagrad Light optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingMDLAdagradLightParameters", name,
        _ctx._post_execution_callbacks, parameters, accumulators, weights,
        benefits, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_mdl_adagrad_light_parameters_eager_fallback(
            parameters, accumulators, weights, benefits, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingMDLAdagradLightParameters", parameters=parameters,
                                                     accumulators=accumulators,
                                                     weights=weights,
                                                     benefits=benefits,
                                                     num_shards=num_shards,
                                                     shard_id=shard_id,
                                                     table_id=table_id,
                                                     table_name=table_name,
                                                     name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingMDLAdagradLightParameters(parameters, accumulators, weights, benefits, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_mdl_adagrad_light_parameters(parameters=parameters, accumulators=accumulators, weights=weights, benefits=benefits, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingMDLAdagradLightParameters.__doc__ = load_tpu_embedding_mdl_adagrad_light_parameters.__doc__
LoadTPUEmbeddingMDLAdagradLightParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingMDLAdagradLightParameters))
tf_export("raw_ops.LoadTPUEmbeddingMDLAdagradLightParameters")(LoadTPUEmbeddingMDLAdagradLightParameters)


def load_tpu_embedding_mdl_adagrad_light_parameters_eager_fallback(parameters, accumulators, weights, benefits, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_mdl_adagrad_light_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  weights = _ops.convert_to_tensor(weights, _dtypes.float32)
  benefits = _ops.convert_to_tensor(benefits, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, weights, benefits]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingMDLAdagradLightParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_momentum_parameters(parameters, momenta, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Momentum embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Momentum optimization algorithm.
    momenta: A `Tensor` of type `float32`.
      Value of momenta used in the Momentum optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingMomentumParameters", name,
        _ctx._post_execution_callbacks, parameters, momenta, "table_id",
        table_id, "table_name", table_name, "num_shards", num_shards,
        "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_momentum_parameters_eager_fallback(
            parameters, momenta, table_id=table_id, table_name=table_name,
            num_shards=num_shards, shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingMomentumParameters", parameters=parameters,
                                              momenta=momenta,
                                              num_shards=num_shards,
                                              shard_id=shard_id,
                                              table_id=table_id,
                                              table_name=table_name,
                                              name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingMomentumParameters(parameters, momenta, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_momentum_parameters(parameters=parameters, momenta=momenta, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingMomentumParameters.__doc__ = load_tpu_embedding_momentum_parameters.__doc__
LoadTPUEmbeddingMomentumParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingMomentumParameters))
tf_export("raw_ops.LoadTPUEmbeddingMomentumParameters")(LoadTPUEmbeddingMomentumParameters)


def load_tpu_embedding_momentum_parameters_eager_fallback(parameters, momenta, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_momentum_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  momenta = _ops.convert_to_tensor(momenta, _dtypes.float32)
  _inputs_flat = [parameters, momenta]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingMomentumParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_momentum_parameters_grad_accum_debug(parameters, momenta, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load Momentum embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the Momentum optimization algorithm.
    momenta: A `Tensor` of type `float32`.
      Value of momenta used in the Momentum optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the Momentum optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingMomentumParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, momenta,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_momentum_parameters_grad_accum_debug_eager_fallback(
            parameters, momenta, gradient_accumulators, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingMomentumParametersGradAccumDebug", parameters=parameters,
                                                            momenta=momenta,
                                                            gradient_accumulators=gradient_accumulators,
                                                            num_shards=num_shards,
                                                            shard_id=shard_id,
                                                            table_id=table_id,
                                                            table_name=table_name,
                                                            name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingMomentumParametersGradAccumDebug(parameters, momenta, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_momentum_parameters_grad_accum_debug(parameters=parameters, momenta=momenta, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingMomentumParametersGradAccumDebug.__doc__ = load_tpu_embedding_momentum_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingMomentumParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingMomentumParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingMomentumParametersGradAccumDebug")(LoadTPUEmbeddingMomentumParametersGradAccumDebug)


def load_tpu_embedding_momentum_parameters_grad_accum_debug_eager_fallback(parameters, momenta, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_momentum_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  momenta = _ops.convert_to_tensor(momenta, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, momenta, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingMomentumParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_proximal_adagrad_parameters(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load proximal Adagrad embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the proximal Adagrad optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the proximal Adagrad optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingProximalAdagradParameters", name,
        _ctx._post_execution_callbacks, parameters, accumulators, "table_id",
        table_id, "table_name", table_name, "num_shards", num_shards,
        "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_proximal_adagrad_parameters_eager_fallback(
            parameters, accumulators, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingProximalAdagradParameters", parameters=parameters,
                                                     accumulators=accumulators,
                                                     num_shards=num_shards,
                                                     shard_id=shard_id,
                                                     table_id=table_id,
                                                     table_name=table_name,
                                                     name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingProximalAdagradParameters(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_proximal_adagrad_parameters(parameters=parameters, accumulators=accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingProximalAdagradParameters.__doc__ = load_tpu_embedding_proximal_adagrad_parameters.__doc__
LoadTPUEmbeddingProximalAdagradParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingProximalAdagradParameters))
tf_export("raw_ops.LoadTPUEmbeddingProximalAdagradParameters")(LoadTPUEmbeddingProximalAdagradParameters)


def load_tpu_embedding_proximal_adagrad_parameters_eager_fallback(parameters, accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_proximal_adagrad_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingProximalAdagradParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load proximal Adagrad embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the proximal Adagrad optimization algorithm.
    accumulators: A `Tensor` of type `float32`.
      Value of accumulators used in the proximal Adagrad optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the proximal Adagrad optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, accumulators,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_eager_fallback(
            parameters, accumulators, gradient_accumulators,
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", parameters=parameters,
                                                                   accumulators=accumulators,
                                                                   gradient_accumulators=gradient_accumulators,
                                                                   num_shards=num_shards,
                                                                   shard_id=shard_id,
                                                                   table_id=table_id,
                                                                   table_name=table_name,
                                                                   name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(parameters=parameters, accumulators=accumulators, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug.__doc__ = load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug")(LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug)


def load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_eager_fallback(parameters, accumulators, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  accumulators = _ops.convert_to_tensor(accumulators, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, accumulators, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_rms_prop_parameters(parameters, ms, mom, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load RMSProp embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the RMSProp optimization algorithm.
    ms: A `Tensor` of type `float32`.
      Value of ms used in the RMSProp optimization algorithm.
    mom: A `Tensor` of type `float32`.
      Value of mom used in the RMSProp optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingRMSPropParameters", name,
        _ctx._post_execution_callbacks, parameters, ms, mom, "table_id",
        table_id, "table_name", table_name, "num_shards", num_shards,
        "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_rms_prop_parameters_eager_fallback(
            parameters, ms, mom, table_id=table_id, table_name=table_name,
            num_shards=num_shards, shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingRMSPropParameters", parameters=parameters, ms=ms,
                                             mom=mom, num_shards=num_shards,
                                             shard_id=shard_id,
                                             table_id=table_id,
                                             table_name=table_name, name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingRMSPropParameters(parameters, ms, mom, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_rms_prop_parameters(parameters=parameters, ms=ms, mom=mom, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingRMSPropParameters.__doc__ = load_tpu_embedding_rms_prop_parameters.__doc__
LoadTPUEmbeddingRMSPropParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingRMSPropParameters))
tf_export("raw_ops.LoadTPUEmbeddingRMSPropParameters")(LoadTPUEmbeddingRMSPropParameters)


def load_tpu_embedding_rms_prop_parameters_eager_fallback(parameters, ms, mom, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_rms_prop_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  ms = _ops.convert_to_tensor(ms, _dtypes.float32)
  mom = _ops.convert_to_tensor(mom, _dtypes.float32)
  _inputs_flat = [parameters, ms, mom]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingRMSPropParameters", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_rms_prop_parameters_grad_accum_debug(parameters, ms, mom, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load RMSProp embedding parameters with debug support.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the RMSProp optimization algorithm.
    ms: A `Tensor` of type `float32`.
      Value of ms used in the RMSProp optimization algorithm.
    mom: A `Tensor` of type `float32`.
      Value of mom used in the RMSProp optimization algorithm.
    gradient_accumulators: A `Tensor` of type `float32`.
      Value of gradient_accumulators used in the RMSProp optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingRMSPropParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, parameters, ms, mom,
        gradient_accumulators, "table_id", table_id, "table_name", table_name,
        "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_rms_prop_parameters_grad_accum_debug_eager_fallback(
            parameters, ms, mom, gradient_accumulators, table_id=table_id,
            table_name=table_name, num_shards=num_shards, shard_id=shard_id,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingRMSPropParametersGradAccumDebug", parameters=parameters,
                                                           ms=ms, mom=mom,
                                                           gradient_accumulators=gradient_accumulators,
                                                           num_shards=num_shards,
                                                           shard_id=shard_id,
                                                           table_id=table_id,
                                                           table_name=table_name,
                                                           name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingRMSPropParametersGradAccumDebug(parameters, ms, mom, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_rms_prop_parameters_grad_accum_debug(parameters=parameters, ms=ms, mom=mom, gradient_accumulators=gradient_accumulators, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingRMSPropParametersGradAccumDebug.__doc__ = load_tpu_embedding_rms_prop_parameters_grad_accum_debug.__doc__
LoadTPUEmbeddingRMSPropParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingRMSPropParametersGradAccumDebug))
tf_export("raw_ops.LoadTPUEmbeddingRMSPropParametersGradAccumDebug")(LoadTPUEmbeddingRMSPropParametersGradAccumDebug)


def load_tpu_embedding_rms_prop_parameters_grad_accum_debug_eager_fallback(parameters, ms, mom, gradient_accumulators, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_rms_prop_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  ms = _ops.convert_to_tensor(ms, _dtypes.float32)
  mom = _ops.convert_to_tensor(mom, _dtypes.float32)
  gradient_accumulators = _ops.convert_to_tensor(gradient_accumulators, _dtypes.float32)
  _inputs_flat = [parameters, ms, mom, gradient_accumulators]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingRMSPropParametersGradAccumDebug",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def load_tpu_embedding_stochastic_gradient_descent_parameters(parameters, num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Load SGD embedding parameters.

  An op that loads optimization parameters into HBM for embedding. Must be
  preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
  embedding table configuration. For example, this op is used to install
  parameters that are loaded from a checkpoint before a training loop is
  executed.

  Args:
    parameters: A `Tensor` of type `float32`.
      Value of parameters used in the stochastic gradient descent optimization algorithm.
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LoadTPUEmbeddingStochasticGradientDescentParameters", name,
        _ctx._post_execution_callbacks, parameters, "table_id", table_id,
        "table_name", table_name, "num_shards", num_shards, "shard_id",
        shard_id)
      return _result
    except _core._FallbackException:
      try:
        return load_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallback(
            parameters, table_id=table_id, table_name=table_name,
            num_shards=num_shards, shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoadTPUEmbeddingStochasticGradientDescentParameters", parameters=parameters,
                                                               num_shards=num_shards,
                                                               shard_id=shard_id,
                                                               table_id=table_id,
                                                               table_name=table_name,
                                                               name=name)
  return _op
  _result = None
  return _result

def LoadTPUEmbeddingStochasticGradientDescentParameters(parameters, num_shards, shard_id, table_id=-1, table_name="", name=None):
  return load_tpu_embedding_stochastic_gradient_descent_parameters(parameters=parameters, num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
LoadTPUEmbeddingStochasticGradientDescentParameters.__doc__ = load_tpu_embedding_stochastic_gradient_descent_parameters.__doc__
LoadTPUEmbeddingStochasticGradientDescentParameters = _doc_controls.do_not_generate_docs(_kwarg_only(LoadTPUEmbeddingStochasticGradientDescentParameters))
tf_export("raw_ops.LoadTPUEmbeddingStochasticGradientDescentParameters")(LoadTPUEmbeddingStochasticGradientDescentParameters)


def load_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallback(parameters, num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function load_tpu_embedding_stochastic_gradient_descent_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  parameters = _ops.convert_to_tensor(parameters, _dtypes.float32)
  _inputs_flat = [parameters]
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"LoadTPUEmbeddingStochasticGradientDescentParameters",
                             0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def outfeed_dequeue(dtype, shape, device_ordinal=-1, name=None):
  r"""Retrieves a single tensor from the computation outfeed.

  This operation will block indefinitely until data is available.

  Args:
    dtype: A `tf.DType`. The type of elements in the tensor.
    shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. This should be -1 when the Op
      is running on a TPU device, and >= 0 when the Op is running on the CPU
      device.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "OutfeedDequeue", name, _ctx._post_execution_callbacks, "dtype",
        dtype, "shape", shape, "device_ordinal", device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return outfeed_dequeue_eager_fallback(
            dtype=dtype, shape=shape, device_ordinal=device_ordinal,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  dtype = _execute.make_type(dtype, "dtype")
  shape = _execute.make_shape(shape, "shape")
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "OutfeedDequeue", dtype=dtype, shape=shape,
                          device_ordinal=device_ordinal, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"),
            "device_ordinal", _op.get_attr("device_ordinal"))
  _execute.record_gradient(
      "OutfeedDequeue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def OutfeedDequeue(dtype, shape, device_ordinal=-1, name=None):
  return outfeed_dequeue(dtype=dtype, shape=shape, device_ordinal=device_ordinal, name=name)
OutfeedDequeue.__doc__ = outfeed_dequeue.__doc__
OutfeedDequeue = _doc_controls.do_not_generate_docs(_kwarg_only(OutfeedDequeue))
tf_export("raw_ops.OutfeedDequeue")(OutfeedDequeue)


def outfeed_dequeue_eager_fallback(dtype, shape, device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function outfeed_dequeue
  """
  _ctx = ctx if ctx else _context.context()
  dtype = _execute.make_type(dtype, "dtype")
  shape = _execute.make_shape(shape, "shape")
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _inputs_flat = []
  _attrs = ("dtype", dtype, "shape", shape, "device_ordinal", device_ordinal)
  _result = _execute.execute(b"OutfeedDequeue", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "OutfeedDequeue", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def outfeed_dequeue_tuple(dtypes, shapes, device_ordinal=-1, name=None):
  r"""Retrieve multiple values from the computation outfeed.

  This operation will block indefinitely until data is available. Output `i`
  corresponds to XLA tuple element `i`.

  Args:
    dtypes: A list of `tf.DTypes` that has length `>= 1`.
      The element types of each element in `outputs`.
    shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
      The shapes of each tensor in `outputs`.
    device_ordinal: An optional `int`. Defaults to `-1`.
      The TPU device to use. This should be -1 when the Op
      is running on a TPU device, and >= 0 when the Op is running on the CPU
      device.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `dtypes`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "OutfeedDequeueTuple", name, _ctx._post_execution_callbacks, "dtypes",
        dtypes, "shapes", shapes, "device_ordinal", device_ordinal)
      return _result
    except _core._FallbackException:
      try:
        return outfeed_dequeue_tuple_eager_fallback(
            dtypes=dtypes, shapes=shapes, device_ordinal=device_ordinal,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(dtypes, (list, tuple)):
    raise TypeError(
        "Expected list for 'dtypes' argument to "
        "'outfeed_dequeue_tuple' Op, not %r." % dtypes)
  dtypes = [_execute.make_type(_t, "dtypes") for _t in dtypes]
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'outfeed_dequeue_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _, _, _op = _op_def_lib._apply_op_helper(
        "OutfeedDequeueTuple", dtypes=dtypes, shapes=shapes,
                               device_ordinal=device_ordinal, name=name)
  _result = _op.outputs[:]
  if not _result:
    return _op
  _inputs_flat = _op.inputs
  _attrs = ("dtypes", _op.get_attr("dtypes"), "shapes",
            _op.get_attr("shapes"), "device_ordinal",
            _op.get_attr("device_ordinal"))
  _execute.record_gradient(
      "OutfeedDequeueTuple", _inputs_flat, _attrs, _result, name)
  return _result

def OutfeedDequeueTuple(dtypes, shapes, device_ordinal=-1, name=None):
  return outfeed_dequeue_tuple(dtypes=dtypes, shapes=shapes, device_ordinal=device_ordinal, name=name)
OutfeedDequeueTuple.__doc__ = outfeed_dequeue_tuple.__doc__
OutfeedDequeueTuple = _doc_controls.do_not_generate_docs(_kwarg_only(OutfeedDequeueTuple))
tf_export("raw_ops.OutfeedDequeueTuple")(OutfeedDequeueTuple)


def outfeed_dequeue_tuple_eager_fallback(dtypes, shapes, device_ordinal=-1, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function outfeed_dequeue_tuple
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(dtypes, (list, tuple)):
    raise TypeError(
        "Expected list for 'dtypes' argument to "
        "'outfeed_dequeue_tuple' Op, not %r." % dtypes)
  dtypes = [_execute.make_type(_t, "dtypes") for _t in dtypes]
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'outfeed_dequeue_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if device_ordinal is None:
    device_ordinal = -1
  device_ordinal = _execute.make_int(device_ordinal, "device_ordinal")
  _inputs_flat = []
  _attrs = ("dtypes", dtypes, "shapes", shapes, "device_ordinal",
  device_ordinal)
  _result = _execute.execute(b"OutfeedDequeueTuple", len(dtypes),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "OutfeedDequeueTuple", _inputs_flat, _attrs, _result, name)
  return _result


def outfeed_enqueue(input, name=None):
  r"""Enqueue a Tensor on the computation outfeed.

  Args:
    input: A `Tensor`. A tensor that will be inserted into the outfeed queue.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "OutfeedEnqueue", name, _ctx._post_execution_callbacks, input)
      return _result
    except _core._FallbackException:
      try:
        return outfeed_enqueue_eager_fallback(
            input, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "OutfeedEnqueue", input=input, name=name)
  return _op
  _result = None
  return _result

def OutfeedEnqueue(input, name=None):
  return outfeed_enqueue(input=input, name=name)
OutfeedEnqueue.__doc__ = outfeed_enqueue.__doc__
OutfeedEnqueue = _doc_controls.do_not_generate_docs(_kwarg_only(OutfeedEnqueue))
tf_export("raw_ops.OutfeedEnqueue")(OutfeedEnqueue)


def outfeed_enqueue_eager_fallback(input, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function outfeed_enqueue
  """
  _ctx = ctx if ctx else _context.context()
  _attr_dtype, (input,) = _execute.args_to_matching_eager([input], _ctx)
  _inputs_flat = [input]
  _attrs = ("dtype", _attr_dtype)
  _result = _execute.execute(b"OutfeedEnqueue", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def outfeed_enqueue_tuple(inputs, name=None):
  r"""Enqueue multiple Tensor values on the computation outfeed.

  Args:
    inputs: A list of `Tensor` objects.
      A list of tensors that will be inserted into the outfeed queue as an
      XLA tuple.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "OutfeedEnqueueTuple", name, _ctx._post_execution_callbacks, inputs)
      return _result
    except _core._FallbackException:
      try:
        return outfeed_enqueue_tuple_eager_fallback(
            inputs, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "OutfeedEnqueueTuple", inputs=inputs, name=name)
  return _op
  _result = None
  return _result

def OutfeedEnqueueTuple(inputs, name=None):
  return outfeed_enqueue_tuple(inputs=inputs, name=name)
OutfeedEnqueueTuple.__doc__ = outfeed_enqueue_tuple.__doc__
OutfeedEnqueueTuple = _doc_controls.do_not_generate_docs(_kwarg_only(OutfeedEnqueueTuple))
tf_export("raw_ops.OutfeedEnqueueTuple")(OutfeedEnqueueTuple)


def outfeed_enqueue_tuple_eager_fallback(inputs, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function outfeed_enqueue_tuple
  """
  _ctx = ctx if ctx else _context.context()
  _attr_dtypes, inputs = _execute.convert_to_mixed_eager_tensors(inputs, _ctx)
  _inputs_flat = list(inputs)
  _attrs = ("dtypes", _attr_dtypes)
  _result = _execute.execute(b"OutfeedEnqueueTuple", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def prelinearize(input, shape=[], layout=[], name=None):
  r"""An op which linearizes one Tensor value to an opaque variant tensor.

  Args:
    input: A `Tensor`. A tensor that will be linearized.
    shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
      The shape of the tensor.
    layout: An optional list of `ints`. Defaults to `[]`.
      A vector holding the requested layout in minor-to-major sequence. If a layout
      attribute is passed but its values are all -1 the layout will be computed by
      the infeed operation.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "Prelinearize", name, _ctx._post_execution_callbacks, input, "shape",
        shape, "layout", layout)
      return _result
    except _core._FallbackException:
      try:
        return prelinearize_eager_fallback(
            input, shape=shape, layout=layout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if shape is None:
    shape = []
  shape = _execute.make_shape(shape, "shape")
  if layout is None:
    layout = []
  if not isinstance(layout, (list, tuple)):
    raise TypeError(
        "Expected list for 'layout' argument to "
        "'prelinearize' Op, not %r." % layout)
  layout = [_execute.make_int(_i, "layout") for _i in layout]
  _, _, _op = _op_def_lib._apply_op_helper(
        "Prelinearize", input=input, shape=shape, layout=layout, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"),
            "layout", _op.get_attr("layout"))
  _execute.record_gradient(
      "Prelinearize", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def Prelinearize(input, shape=[], layout=[], name=None):
  return prelinearize(input=input, shape=shape, layout=layout, name=name)
Prelinearize.__doc__ = prelinearize.__doc__
Prelinearize = _doc_controls.do_not_generate_docs(_kwarg_only(Prelinearize))
tf_export("raw_ops.Prelinearize")(Prelinearize)


def prelinearize_eager_fallback(input, shape=[], layout=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function prelinearize
  """
  _ctx = ctx if ctx else _context.context()
  if shape is None:
    shape = []
  shape = _execute.make_shape(shape, "shape")
  if layout is None:
    layout = []
  if not isinstance(layout, (list, tuple)):
    raise TypeError(
        "Expected list for 'layout' argument to "
        "'prelinearize' Op, not %r." % layout)
  layout = [_execute.make_int(_i, "layout") for _i in layout]
  _attr_dtype, (input,) = _execute.args_to_matching_eager([input], _ctx)
  _inputs_flat = [input]
  _attrs = ("dtype", _attr_dtype, "shape", shape, "layout", layout)
  _result = _execute.execute(b"Prelinearize", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "Prelinearize", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def prelinearize_tuple(inputs, shapes, layouts=[], name=None):
  r"""An op which linearizes multiple Tensor values to an opaque variant tensor.

  Args:
    inputs: A list of `Tensor` objects.
      A list of tensors that will be provided using the infeed mechanism.
    shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
      The shapes of each tensor in `inputs`.
    layouts: An optional list of `ints`. Defaults to `[]`.
      A vector holding the requested layout in minor-to-major sequence for all the
      tuple shapes in the order the shapes appear in the "shapes" input. The layout
      elements for a sub-shape can be set to -1 in which case the corresponding layout
      will be computed by the infeed operation.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `variant`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "PrelinearizeTuple", name, _ctx._post_execution_callbacks, inputs,
        "shapes", shapes, "layouts", layouts)
      return _result
    except _core._FallbackException:
      try:
        return prelinearize_tuple_eager_fallback(
            inputs, shapes=shapes, layouts=layouts, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'prelinearize_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if layouts is None:
    layouts = []
  if not isinstance(layouts, (list, tuple)):
    raise TypeError(
        "Expected list for 'layouts' argument to "
        "'prelinearize_tuple' Op, not %r." % layouts)
  layouts = [_execute.make_int(_i, "layouts") for _i in layouts]
  _, _, _op = _op_def_lib._apply_op_helper(
        "PrelinearizeTuple", inputs=inputs, shapes=shapes, layouts=layouts,
                             name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtypes", _op.get_attr("dtypes"), "shapes",
            _op.get_attr("shapes"), "layouts", _op.get_attr("layouts"))
  _execute.record_gradient(
      "PrelinearizeTuple", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def PrelinearizeTuple(inputs, shapes, layouts=[], name=None):
  return prelinearize_tuple(inputs=inputs, shapes=shapes, layouts=layouts, name=name)
PrelinearizeTuple.__doc__ = prelinearize_tuple.__doc__
PrelinearizeTuple = _doc_controls.do_not_generate_docs(_kwarg_only(PrelinearizeTuple))
tf_export("raw_ops.PrelinearizeTuple")(PrelinearizeTuple)


def prelinearize_tuple_eager_fallback(inputs, shapes, layouts=[], name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function prelinearize_tuple
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'prelinearize_tuple' Op, not %r." % shapes)
  shapes = [_execute.make_shape(_s, "shapes") for _s in shapes]
  if layouts is None:
    layouts = []
  if not isinstance(layouts, (list, tuple)):
    raise TypeError(
        "Expected list for 'layouts' argument to "
        "'prelinearize_tuple' Op, not %r." % layouts)
  layouts = [_execute.make_int(_i, "layouts") for _i in layouts]
  _attr_dtypes, inputs = _execute.convert_to_mixed_eager_tensors(inputs, _ctx)
  _inputs_flat = list(inputs)
  _attrs = ("dtypes", _attr_dtypes, "shapes", shapes, "layouts", layouts)
  _result = _execute.execute(b"PrelinearizeTuple", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "PrelinearizeTuple", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def recv_tpu_embedding_activations(num_outputs, config, name=None):
  r"""An op that receives embedding activations on the TPU.

  The TPU system performs the embedding lookups and aggregations specified by
  the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The
  results of these aggregations are visible to the Tensorflow Graph as the
  outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing
  one Tensor of activations per table specified in the model. There can be at
  most one RecvTPUEmbeddingActivations op in the TPU graph.

  Args:
    num_outputs: An `int` that is `>= 1`.
      The number of output activation tensors, equal to the number of
      embedding tables in the model.
    config: A `string`. Serialized TPUEmbeddingConfiguration proto.
    name: A name for the operation (optional).

  Returns:
    A list of `num_outputs` `Tensor` objects with type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RecvTPUEmbeddingActivations", name, _ctx._post_execution_callbacks,
        "num_outputs", num_outputs, "config", config)
      return _result
    except _core._FallbackException:
      try:
        return recv_tpu_embedding_activations_eager_fallback(
            num_outputs=num_outputs, config=config, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_outputs = _execute.make_int(num_outputs, "num_outputs")
  config = _execute.make_str(config, "config")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RecvTPUEmbeddingActivations", num_outputs=num_outputs, config=config,
                                       name=name)
  _result = _op.outputs[:]
  if not _result:
    return _op
  _inputs_flat = _op.inputs
  _attrs = ("num_outputs", _op.get_attr("num_outputs"), "config",
            _op.get_attr("config"))
  _execute.record_gradient(
      "RecvTPUEmbeddingActivations", _inputs_flat, _attrs, _result, name)
  return _result

def RecvTPUEmbeddingActivations(num_outputs, config, name=None):
  return recv_tpu_embedding_activations(num_outputs=num_outputs, config=config, name=name)
RecvTPUEmbeddingActivations.__doc__ = recv_tpu_embedding_activations.__doc__
RecvTPUEmbeddingActivations = _doc_controls.do_not_generate_docs(_kwarg_only(RecvTPUEmbeddingActivations))
tf_export("raw_ops.RecvTPUEmbeddingActivations")(RecvTPUEmbeddingActivations)


def recv_tpu_embedding_activations_eager_fallback(num_outputs, config, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function recv_tpu_embedding_activations
  """
  _ctx = ctx if ctx else _context.context()
  num_outputs = _execute.make_int(num_outputs, "num_outputs")
  config = _execute.make_str(config, "config")
  _inputs_flat = []
  _attrs = ("num_outputs", num_outputs, "config", config)
  _result = _execute.execute(b"RecvTPUEmbeddingActivations", num_outputs,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RecvTPUEmbeddingActivations", _inputs_flat, _attrs, _result, name)
  return _result


_retrieve_tpu_embedding_adam_parameters_outputs = ["parameters", "momenta",
                                                  "velocities"]
_RetrieveTPUEmbeddingADAMParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingADAMParameters",
    _retrieve_tpu_embedding_adam_parameters_outputs)


def retrieve_tpu_embedding_adam_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve ADAM embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, momenta, velocities).

    parameters: A `Tensor` of type `float32`.
    momenta: A `Tensor` of type `float32`.
    velocities: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingADAMParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingADAMParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adam_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingADAMParameters", num_shards=num_shards,
                                              shard_id=shard_id,
                                              table_id=table_id,
                                              table_name=table_name,
                                              name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingADAMParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingADAMParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingADAMParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adam_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingADAMParameters.__doc__ = retrieve_tpu_embedding_adam_parameters.__doc__
RetrieveTPUEmbeddingADAMParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingADAMParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingADAMParameters")(RetrieveTPUEmbeddingADAMParameters)


def retrieve_tpu_embedding_adam_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adam_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingADAMParameters", 3,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingADAMParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingADAMParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_adam_parameters_grad_accum_debug_outputs = ["parameters",
                                                                   "momenta",
                                                                   "velocities",
                                                                   "gradient_accumulators"]
_RetrieveTPUEmbeddingADAMParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingADAMParametersGradAccumDebug",
    _retrieve_tpu_embedding_adam_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_adam_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve ADAM embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, momenta, velocities, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    momenta: A `Tensor` of type `float32`.
    velocities: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingADAMParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adam_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", num_shards=num_shards,
                                                            shard_id=shard_id,
                                                            table_id=table_id,
                                                            table_name=table_name,
                                                            name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingADAMParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingADAMParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adam_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingADAMParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_adam_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingADAMParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingADAMParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingADAMParametersGradAccumDebug")(RetrieveTPUEmbeddingADAMParametersGradAccumDebug)


def retrieve_tpu_embedding_adam_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adam_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingADAMParametersGradAccumDebug",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingADAMParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_adadelta_parameters_outputs = ["parameters",
                                                      "accumulators",
                                                      "updates"]
_RetrieveTPUEmbeddingAdadeltaParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingAdadeltaParameters",
    _retrieve_tpu_embedding_adadelta_parameters_outputs)


def retrieve_tpu_embedding_adadelta_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Adadelta embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, updates).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    updates: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingAdadeltaParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingAdadeltaParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adadelta_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingAdadeltaParameters", num_shards=num_shards,
                                                  shard_id=shard_id,
                                                  table_id=table_id,
                                                  table_name=table_name,
                                                  name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdadeltaParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdadeltaParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingAdadeltaParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adadelta_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingAdadeltaParameters.__doc__ = retrieve_tpu_embedding_adadelta_parameters.__doc__
RetrieveTPUEmbeddingAdadeltaParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingAdadeltaParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingAdadeltaParameters")(RetrieveTPUEmbeddingAdadeltaParameters)


def retrieve_tpu_embedding_adadelta_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adadelta_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingAdadeltaParameters", 3,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdadeltaParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdadeltaParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug_outputs = ["parameters",
                                                                       "accumulators",
                                                                       "updates",
                                                                       "gradient_accumulators"]
_RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug",
    _retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Adadelta embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, updates, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    updates: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", num_shards=num_shards,
                                                                shard_id=shard_id,
                                                                table_id=table_id,
                                                                table_name=table_name,
                                                                name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug")(RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug)


def retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_adagrad_parameters_outputs = ["parameters",
                                                     "accumulators"]
_RetrieveTPUEmbeddingAdagradParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingAdagradParameters",
    _retrieve_tpu_embedding_adagrad_parameters_outputs)


def retrieve_tpu_embedding_adagrad_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Adagrad embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingAdagradParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingAdagradParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adagrad_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingAdagradParameters", num_shards=num_shards,
                                                 shard_id=shard_id,
                                                 table_id=table_id,
                                                 table_name=table_name,
                                                 name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdagradParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdagradParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingAdagradParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adagrad_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingAdagradParameters.__doc__ = retrieve_tpu_embedding_adagrad_parameters.__doc__
RetrieveTPUEmbeddingAdagradParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingAdagradParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingAdagradParameters")(RetrieveTPUEmbeddingAdagradParameters)


def retrieve_tpu_embedding_adagrad_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adagrad_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingAdagradParameters", 2,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdagradParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdagradParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug_outputs = ["parameters",
                                                                      "accumulators",
                                                                      "gradient_accumulators"]
_RetrieveTPUEmbeddingAdagradParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug",
    _retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Adagrad embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingAdagradParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", num_shards=num_shards,
                                                               shard_id=shard_id,
                                                               table_id=table_id,
                                                               table_name=table_name,
                                                               name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdagradParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingAdagradParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingAdagradParametersGradAccumDebug")(RetrieveTPUEmbeddingAdagradParametersGradAccumDebug)


def retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingAdagradParametersGradAccumDebug",
                             3, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingAdagradParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_centered_rms_prop_parameters_outputs = ["parameters",
                                                               "ms", "mom",
                                                               "mg"]
_RetrieveTPUEmbeddingCenteredRMSPropParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingCenteredRMSPropParameters",
    _retrieve_tpu_embedding_centered_rms_prop_parameters_outputs)


def retrieve_tpu_embedding_centered_rms_prop_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve centered RMSProp embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, ms, mom, mg).

    parameters: A `Tensor` of type `float32`.
    ms: A `Tensor` of type `float32`.
    mom: A `Tensor` of type `float32`.
    mg: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingCenteredRMSPropParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingCenteredRMSPropParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_centered_rms_prop_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingCenteredRMSPropParameters", num_shards=num_shards,
                                                         shard_id=shard_id,
                                                         table_id=table_id,
                                                         table_name=table_name,
                                                         name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingCenteredRMSPropParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingCenteredRMSPropParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingCenteredRMSPropParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_centered_rms_prop_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingCenteredRMSPropParameters.__doc__ = retrieve_tpu_embedding_centered_rms_prop_parameters.__doc__
RetrieveTPUEmbeddingCenteredRMSPropParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingCenteredRMSPropParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingCenteredRMSPropParameters")(RetrieveTPUEmbeddingCenteredRMSPropParameters)


def retrieve_tpu_embedding_centered_rms_prop_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_centered_rms_prop_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingCenteredRMSPropParameters",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingCenteredRMSPropParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingCenteredRMSPropParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_ftrl_parameters_outputs = ["parameters",
                                                  "accumulators", "linears"]
_RetrieveTPUEmbeddingFTRLParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingFTRLParameters",
    _retrieve_tpu_embedding_ftrl_parameters_outputs)


def retrieve_tpu_embedding_ftrl_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve FTRL embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, linears).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    linears: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingFTRLParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingFTRLParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_ftrl_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingFTRLParameters", num_shards=num_shards,
                                              shard_id=shard_id,
                                              table_id=table_id,
                                              table_name=table_name,
                                              name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingFTRLParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingFTRLParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingFTRLParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_ftrl_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingFTRLParameters.__doc__ = retrieve_tpu_embedding_ftrl_parameters.__doc__
RetrieveTPUEmbeddingFTRLParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingFTRLParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingFTRLParameters")(RetrieveTPUEmbeddingFTRLParameters)


def retrieve_tpu_embedding_ftrl_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_ftrl_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingFTRLParameters", 3,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingFTRLParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingFTRLParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug_outputs = ["parameters",
                                                                   "accumulators",
                                                                   "linears",
                                                                   "gradient_accumulators"]
_RetrieveTPUEmbeddingFTRLParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug",
    _retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve FTRL embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, linears, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    linears: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingFTRLParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", num_shards=num_shards,
                                                            shard_id=shard_id,
                                                            table_id=table_id,
                                                            table_name=table_name,
                                                            name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingFTRLParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingFTRLParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingFTRLParametersGradAccumDebug")(RetrieveTPUEmbeddingFTRLParametersGradAccumDebug)


def retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingFTRLParametersGradAccumDebug",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingFTRLParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_mdl_adagrad_light_parameters_outputs = ["parameters",
                                                               "accumulators",
                                                               "weights",
                                                               "benefits"]
_RetrieveTPUEmbeddingMDLAdagradLightParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingMDLAdagradLightParameters",
    _retrieve_tpu_embedding_mdl_adagrad_light_parameters_outputs)


def retrieve_tpu_embedding_mdl_adagrad_light_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve MDL Adagrad Light embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, weights, benefits).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    weights: A `Tensor` of type `float32`.
    benefits: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingMDLAdagradLightParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingMDLAdagradLightParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_mdl_adagrad_light_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingMDLAdagradLightParameters", num_shards=num_shards,
                                                         shard_id=shard_id,
                                                         table_id=table_id,
                                                         table_name=table_name,
                                                         name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMDLAdagradLightParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMDLAdagradLightParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingMDLAdagradLightParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_mdl_adagrad_light_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingMDLAdagradLightParameters.__doc__ = retrieve_tpu_embedding_mdl_adagrad_light_parameters.__doc__
RetrieveTPUEmbeddingMDLAdagradLightParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingMDLAdagradLightParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingMDLAdagradLightParameters")(RetrieveTPUEmbeddingMDLAdagradLightParameters)


def retrieve_tpu_embedding_mdl_adagrad_light_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_mdl_adagrad_light_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingMDLAdagradLightParameters",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMDLAdagradLightParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMDLAdagradLightParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_momentum_parameters_outputs = ["parameters",
                                                      "momenta"]
_RetrieveTPUEmbeddingMomentumParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingMomentumParameters",
    _retrieve_tpu_embedding_momentum_parameters_outputs)


def retrieve_tpu_embedding_momentum_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Momentum embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, momenta).

    parameters: A `Tensor` of type `float32`.
    momenta: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingMomentumParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingMomentumParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_momentum_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingMomentumParameters", num_shards=num_shards,
                                                  shard_id=shard_id,
                                                  table_id=table_id,
                                                  table_name=table_name,
                                                  name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMomentumParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMomentumParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingMomentumParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_momentum_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingMomentumParameters.__doc__ = retrieve_tpu_embedding_momentum_parameters.__doc__
RetrieveTPUEmbeddingMomentumParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingMomentumParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingMomentumParameters")(RetrieveTPUEmbeddingMomentumParameters)


def retrieve_tpu_embedding_momentum_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_momentum_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingMomentumParameters", 2,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMomentumParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMomentumParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_momentum_parameters_grad_accum_debug_outputs = ["parameters",
                                                                       "momenta",
                                                                       "gradient_accumulators"]
_RetrieveTPUEmbeddingMomentumParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug",
    _retrieve_tpu_embedding_momentum_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_momentum_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve Momentum embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, momenta, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    momenta: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingMomentumParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_momentum_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", num_shards=num_shards,
                                                                shard_id=shard_id,
                                                                table_id=table_id,
                                                                table_name=table_name,
                                                                name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMomentumParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_momentum_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_momentum_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingMomentumParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingMomentumParametersGradAccumDebug")(RetrieveTPUEmbeddingMomentumParametersGradAccumDebug)


def retrieve_tpu_embedding_momentum_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_momentum_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingMomentumParametersGradAccumDebug",
                             3, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingMomentumParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_proximal_adagrad_parameters_outputs = ["parameters",
                                                              "accumulators"]
_RetrieveTPUEmbeddingProximalAdagradParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingProximalAdagradParameters",
    _retrieve_tpu_embedding_proximal_adagrad_parameters_outputs)


def retrieve_tpu_embedding_proximal_adagrad_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve proximal Adagrad embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingProximalAdagradParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingProximalAdagradParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_proximal_adagrad_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingProximalAdagradParameters", num_shards=num_shards,
                                                         shard_id=shard_id,
                                                         table_id=table_id,
                                                         table_name=table_name,
                                                         name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingProximalAdagradParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingProximalAdagradParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingProximalAdagradParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_proximal_adagrad_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingProximalAdagradParameters.__doc__ = retrieve_tpu_embedding_proximal_adagrad_parameters.__doc__
RetrieveTPUEmbeddingProximalAdagradParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingProximalAdagradParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters")(RetrieveTPUEmbeddingProximalAdagradParameters)


def retrieve_tpu_embedding_proximal_adagrad_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_proximal_adagrad_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingProximalAdagradParameters",
                             2, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingProximalAdagradParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingProximalAdagradParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_outputs = ["parameters", "accumulators",
                                                                               "gradient_accumulators"]
_RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug",
    _retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve proximal Adagrad embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, accumulators, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    accumulators: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", num_shards=num_shards,
                                                                       shard_id=shard_id,
                                                                       table_id=table_id,
                                                                       table_name=table_name,
                                                                       name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug")(RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug)


def retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug",
                             3, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugOutput._make(_result)
  return _result


_retrieve_tpu_embedding_rms_prop_parameters_outputs = ["parameters", "ms",
                                                      "mom"]
_RetrieveTPUEmbeddingRMSPropParametersOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingRMSPropParameters",
    _retrieve_tpu_embedding_rms_prop_parameters_outputs)


def retrieve_tpu_embedding_rms_prop_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve RMSProp embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, ms, mom).

    parameters: A `Tensor` of type `float32`.
    ms: A `Tensor` of type `float32`.
    mom: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingRMSPropParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingRMSPropParametersOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_rms_prop_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingRMSPropParameters", num_shards=num_shards,
                                                 shard_id=shard_id,
                                                 table_id=table_id,
                                                 table_name=table_name,
                                                 name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingRMSPropParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingRMSPropParametersOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingRMSPropParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_rms_prop_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingRMSPropParameters.__doc__ = retrieve_tpu_embedding_rms_prop_parameters.__doc__
RetrieveTPUEmbeddingRMSPropParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingRMSPropParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingRMSPropParameters")(RetrieveTPUEmbeddingRMSPropParameters)


def retrieve_tpu_embedding_rms_prop_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_rms_prop_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingRMSPropParameters", 3,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingRMSPropParameters", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingRMSPropParametersOutput._make(_result)
  return _result


_retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug_outputs = ["parameters",
                                                                       "ms",
                                                                       "mom",
                                                                       "gradient_accumulators"]
_RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugOutput = _collections.namedtuple(
    "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug",
    _retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug_outputs)


def retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve RMSProp embedding parameters with debug support.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (parameters, ms, mom, gradient_accumulators).

    parameters: A `Tensor` of type `float32`.
    ms: A `Tensor` of type `float32`.
    mom: A `Tensor` of type `float32`.
    gradient_accumulators: A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      _result = _RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", num_shards=num_shards,
                                                               shard_id=shard_id,
                                                               table_id=table_id,
                                                               table_name=table_name,
                                                               name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugOutput._make(_result)
  return _result

def RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug.__doc__ = retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug.__doc__
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug))
tf_export("raw_ops.RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug")(RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug)


def retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug",
                             4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", _inputs_flat, _attrs, _result, name)
  _result = _RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugOutput._make(_result)
  return _result


def retrieve_tpu_embedding_stochastic_gradient_descent_parameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  r"""Retrieve SGD embedding parameters.

  An op that retrieves optimization parameters from embedding to host
  memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
  the correct embedding table configuration. For example, this op is
  used to retrieve updated parameters before saving a checkpoint.

  Args:
    num_shards: An `int`.
    shard_id: An `int`.
    table_id: An optional `int` that is `>= -1`. Defaults to `-1`.
    table_name: An optional `string`. Defaults to `""`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RetrieveTPUEmbeddingStochasticGradientDescentParameters", name,
        _ctx._post_execution_callbacks, "table_id", table_id, "table_name",
        table_name, "num_shards", num_shards, "shard_id", shard_id)
      return _result
    except _core._FallbackException:
      try:
        return retrieve_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallback(
            table_id=table_id, table_name=table_name, num_shards=num_shards,
            shard_id=shard_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RetrieveTPUEmbeddingStochasticGradientDescentParameters", num_shards=num_shards,
                                                                   shard_id=shard_id,
                                                                   table_id=table_id,
                                                                   table_name=table_name,
                                                                   name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "table_name",
            _op.get_attr("table_name"), "num_shards",
            _op.get_attr("num_shards"), "shard_id", _op.get_attr("shard_id"))
  _execute.record_gradient(
      "RetrieveTPUEmbeddingStochasticGradientDescentParameters", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RetrieveTPUEmbeddingStochasticGradientDescentParameters(num_shards, shard_id, table_id=-1, table_name="", name=None):
  return retrieve_tpu_embedding_stochastic_gradient_descent_parameters(num_shards=num_shards, shard_id=shard_id, table_id=table_id, table_name=table_name, name=name)
RetrieveTPUEmbeddingStochasticGradientDescentParameters.__doc__ = retrieve_tpu_embedding_stochastic_gradient_descent_parameters.__doc__
RetrieveTPUEmbeddingStochasticGradientDescentParameters = _doc_controls.do_not_generate_docs(_kwarg_only(RetrieveTPUEmbeddingStochasticGradientDescentParameters))
tf_export("raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParameters")(RetrieveTPUEmbeddingStochasticGradientDescentParameters)


def retrieve_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallback(num_shards, shard_id, table_id=-1, table_name="", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function retrieve_tpu_embedding_stochastic_gradient_descent_parameters
  """
  _ctx = ctx if ctx else _context.context()
  num_shards = _execute.make_int(num_shards, "num_shards")
  shard_id = _execute.make_int(shard_id, "shard_id")
  if table_id is None:
    table_id = -1
  table_id = _execute.make_int(table_id, "table_id")
  if table_name is None:
    table_name = ""
  table_name = _execute.make_str(table_name, "table_name")
  _inputs_flat = []
  _attrs = ("table_id", table_id, "table_name", table_name, "num_shards",
  num_shards, "shard_id", shard_id)
  _result = _execute.execute(b"RetrieveTPUEmbeddingStochasticGradientDescentParameters",
                             1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "RetrieveTPUEmbeddingStochasticGradientDescentParameters", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def send_tpu_embedding_gradients(inputs, learning_rates, config, name=None):
  r"""Performs gradient updates of embedding tables.

  Args:
    inputs: A list of at least 1 `Tensor` objects with type `float32`.
      A TensorList of gradients with which to update embedding tables.
      This argument has the same length and shapes as the return value of
      RecvTPUEmbeddingActivations, but contains gradients of the model's loss
      with respect to the embedding activations. The embedding tables are updated
      from these gradients via the optimizer specified in the TPU embedding
      configuration given to tpu.initialize_system.
    learning_rates: A list of `Tensor` objects with type `float32`.
      A TensorList of float32 scalars, one for each dynamic learning
      rate tag: see the comments in
      //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto.
      Multiple tables can share the same dynamic learning rate tag as specified
      in the configuration. If the learning rates for all tables are constant,
      this list should be empty.
    config: A `string`. Serialized TPUEmbeddingConfiguration proto.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "SendTPUEmbeddingGradients", name, _ctx._post_execution_callbacks,
        inputs, learning_rates, "config", config)
      return _result
    except _core._FallbackException:
      try:
        return send_tpu_embedding_gradients_eager_fallback(
            inputs, learning_rates, config=config, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'send_tpu_embedding_gradients' Op, not %r." % inputs)
  _attr_N = len(inputs)
  if not isinstance(learning_rates, (list, tuple)):
    raise TypeError(
        "Expected list for 'learning_rates' argument to "
        "'send_tpu_embedding_gradients' Op, not %r." % learning_rates)
  _attr_NN = len(learning_rates)
  config = _execute.make_str(config, "config")
  _, _, _op = _op_def_lib._apply_op_helper(
        "SendTPUEmbeddingGradients", inputs=inputs,
                                     learning_rates=learning_rates,
                                     config=config, name=name)
  return _op
  _result = None
  return _result

def SendTPUEmbeddingGradients(inputs, learning_rates, config, name=None):
  return send_tpu_embedding_gradients(inputs=inputs, learning_rates=learning_rates, config=config, name=name)
SendTPUEmbeddingGradients.__doc__ = send_tpu_embedding_gradients.__doc__
SendTPUEmbeddingGradients = _doc_controls.do_not_generate_docs(_kwarg_only(SendTPUEmbeddingGradients))
tf_export("raw_ops.SendTPUEmbeddingGradients")(SendTPUEmbeddingGradients)


def send_tpu_embedding_gradients_eager_fallback(inputs, learning_rates, config, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function send_tpu_embedding_gradients
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'send_tpu_embedding_gradients' Op, not %r." % inputs)
  _attr_N = len(inputs)
  if not isinstance(learning_rates, (list, tuple)):
    raise TypeError(
        "Expected list for 'learning_rates' argument to "
        "'send_tpu_embedding_gradients' Op, not %r." % learning_rates)
  _attr_NN = len(learning_rates)
  config = _execute.make_str(config, "config")
  inputs = _ops.convert_n_to_tensor(inputs, _dtypes.float32)
  learning_rates = _ops.convert_n_to_tensor(learning_rates, _dtypes.float32)
  _inputs_flat = list(inputs) + list(learning_rates)
  _attrs = ("N", _attr_N, "NN", _attr_NN, "config", config)
  _result = _execute.execute(b"SendTPUEmbeddingGradients", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def shutdown_distributed_tpu(name=None):
  r"""Shuts down a running distributed TPU system.

  The op returns an error if no system is running.

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

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "ShutdownDistributedTPU", name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return shutdown_distributed_tpu_eager_fallback(
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "ShutdownDistributedTPU", name=name)
  return _op
  _result = None
  return _result

def ShutdownDistributedTPU(name=None):
  return shutdown_distributed_tpu(name=name)
ShutdownDistributedTPU.__doc__ = shutdown_distributed_tpu.__doc__
ShutdownDistributedTPU = _doc_controls.do_not_generate_docs(_kwarg_only(ShutdownDistributedTPU))
tf_export("raw_ops.ShutdownDistributedTPU")(ShutdownDistributedTPU)


def shutdown_distributed_tpu_eager_fallback(name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function shutdown_distributed_tpu
  """
  _ctx = ctx if ctx else _context.context()
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"ShutdownDistributedTPU", 0,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _result = None
  return _result


def tpu_compilation_result(name=None):
  r"""CompilationResultProto indicating the status of the TPU compilation.

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

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUCompilationResult", name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return tpu_compilation_result_eager_fallback(
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUCompilationResult", name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = None
  _execute.record_gradient(
      "TPUCompilationResult", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def TPUCompilationResult(name=None):
  return tpu_compilation_result(name=name)
TPUCompilationResult.__doc__ = tpu_compilation_result.__doc__
TPUCompilationResult = _doc_controls.do_not_generate_docs(_kwarg_only(TPUCompilationResult))
tf_export("raw_ops.TPUCompilationResult")(TPUCompilationResult)


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


def tpu_embedding_activations(embedding_variable, sliced_activations, table_id, lookup_id, name=None):
  r"""An op enabling differentiation of TPU Embeddings.

  This op simply returns its first input, which is assumed to have been sliced
  from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of
  this op, and its first argument being a trainable Variable, enables automatic
  differentiation of graphs containing embeddings via the TPU Embedding Python
  libraries.

  Args:
    embedding_variable: A `Tensor` of type `float32`.
      A trainable variable, enabling optimizers to find this op.
    sliced_activations: A `Tensor` of type `float32`.
      The embedding activations Tensor to return.
    table_id: An `int` that is `>= 0`.
      The id of the table in the embedding layer configuration from which
      these activations were computed.
    lookup_id: An `int` that is `>= 0`.
      Identifier of the set of embedding indices which produced these
      activations.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `float32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUEmbeddingActivations", name, _ctx._post_execution_callbacks,
        embedding_variable, sliced_activations, "table_id", table_id,
        "lookup_id", lookup_id)
      return _result
    except _core._FallbackException:
      try:
        return tpu_embedding_activations_eager_fallback(
            embedding_variable, sliced_activations, table_id=table_id,
            lookup_id=lookup_id, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  table_id = _execute.make_int(table_id, "table_id")
  lookup_id = _execute.make_int(lookup_id, "lookup_id")
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUEmbeddingActivations", embedding_variable=embedding_variable,
                                   sliced_activations=sliced_activations,
                                   table_id=table_id, lookup_id=lookup_id,
                                   name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("table_id", _op.get_attr("table_id"), "lookup_id",
            _op.get_attr("lookup_id"))
  _execute.record_gradient(
      "TPUEmbeddingActivations", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def TPUEmbeddingActivations(embedding_variable, sliced_activations, table_id, lookup_id, name=None):
  return tpu_embedding_activations(embedding_variable=embedding_variable, sliced_activations=sliced_activations, table_id=table_id, lookup_id=lookup_id, name=name)
TPUEmbeddingActivations.__doc__ = tpu_embedding_activations.__doc__
TPUEmbeddingActivations = _doc_controls.do_not_generate_docs(_kwarg_only(TPUEmbeddingActivations))
tf_export("raw_ops.TPUEmbeddingActivations")(TPUEmbeddingActivations)


def tpu_embedding_activations_eager_fallback(embedding_variable, sliced_activations, table_id, lookup_id, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tpu_embedding_activations
  """
  _ctx = ctx if ctx else _context.context()
  table_id = _execute.make_int(table_id, "table_id")
  lookup_id = _execute.make_int(lookup_id, "lookup_id")
  embedding_variable = _ops.convert_to_tensor(embedding_variable, _dtypes.float32)
  sliced_activations = _ops.convert_to_tensor(sliced_activations, _dtypes.float32)
  _inputs_flat = [embedding_variable, sliced_activations]
  _attrs = ("table_id", table_id, "lookup_id", lookup_id)
  _result = _execute.execute(b"TPUEmbeddingActivations", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "TPUEmbeddingActivations", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def tpu_ordinal_selector(name=None):
  r"""A TPU core selector Op.

  This Op produces a set of TPU cores (for warm-up) or a single TPU core
  (for regular inference) to execute the TPU program on. The output is
  consumed by TPUPartitionedCall.

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

  Returns:
    A `Tensor` of type `int32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUOrdinalSelector", name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return tpu_ordinal_selector_eager_fallback(
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUOrdinalSelector", name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = None
  _execute.record_gradient(
      "TPUOrdinalSelector", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def TPUOrdinalSelector(name=None):
  return tpu_ordinal_selector(name=name)
TPUOrdinalSelector.__doc__ = tpu_ordinal_selector.__doc__
TPUOrdinalSelector = _doc_controls.do_not_generate_docs(_kwarg_only(TPUOrdinalSelector))
tf_export("raw_ops.TPUOrdinalSelector")(TPUOrdinalSelector)


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


def tpu_partitioned_call(args, device_ordinal, Tout, f, name=None):
  r"""Calls a function placed on a specified TPU device.

  Args:
    args: A list of `Tensor` objects. The arguments to the function.
    device_ordinal: A `Tensor` of type `int32`.
      The TPU device ordinal to run the function on.
    Tout: A list of `tf.DTypes`. The types of the outputs of the function.
    f: A function decorated with @Defun. The function to call.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` objects of type `Tout`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUPartitionedCall", name, _ctx._post_execution_callbacks, args,
        device_ordinal, "Tout", Tout, "f", f)
      return _result
    except _core._FallbackException:
      try:
        return tpu_partitioned_call_eager_fallback(
            args, device_ordinal, Tout=Tout, f=f, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(Tout, (list, tuple)):
    raise TypeError(
        "Expected list for 'Tout' argument to "
        "'tpu_partitioned_call' Op, not %r." % Tout)
  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUPartitionedCall", args=args, device_ordinal=device_ordinal,
                              Tout=Tout, f=f, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("Tin", _op.get_attr("Tin"), "Tout", _op.get_attr("Tout"), "f",
            _op.get_attr("f"))
  _execute.record_gradient(
      "TPUPartitionedCall", _inputs_flat, _attrs, _result, name)
  return _result

def TPUPartitionedCall(args, device_ordinal, Tout, f, name=None):
  return tpu_partitioned_call(args=args, device_ordinal=device_ordinal, Tout=Tout, f=f, name=name)
TPUPartitionedCall.__doc__ = tpu_partitioned_call.__doc__
TPUPartitionedCall = _doc_controls.do_not_generate_docs(_kwarg_only(TPUPartitionedCall))
tf_export("raw_ops.TPUPartitionedCall")(TPUPartitionedCall)


def tpu_partitioned_call_eager_fallback(args, device_ordinal, Tout, f, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tpu_partitioned_call
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(Tout, (list, tuple)):
    raise TypeError(
        "Expected list for 'Tout' argument to "
        "'tpu_partitioned_call' Op, not %r." % Tout)
  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
  _attr_Tin, args = _execute.convert_to_mixed_eager_tensors(args, _ctx)
  device_ordinal = _ops.convert_to_tensor(device_ordinal, _dtypes.int32)
  _inputs_flat = list(args) + [device_ordinal]
  _attrs = ("Tin", _attr_Tin, "Tout", Tout, "f", f)
  _result = _execute.execute(b"TPUPartitionedCall", len(Tout),
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "TPUPartitionedCall", _inputs_flat, _attrs, _result, name)
  return _result


def tpu_replicate_metadata(num_replicas, num_cores_per_replica=1, topology="", use_tpu=True, device_assignment=[], computation_shape=[], host_compute_core=[], padding_map=[], step_marker_location="STEP_MARK_AT_ENTRY", name=None):
  r"""Metadata indicaitng how the TPU computation should be replicated.

  Args:
    num_replicas: An `int` that is `>= 0`.
      Number of replicas of the computation
    num_cores_per_replica: An optional `int`. Defaults to `1`.
      Number of cores per replica. Used for model parallelism.
    topology: An optional `string`. Defaults to `""`.
      TopologyProto indicating the topology of the TPU pod slice.
    use_tpu: An optional `bool`. Defaults to `True`.
      Whether to place the computation on the TPU.
    device_assignment: An optional list of `ints`. Defaults to `[]`.
      The assignment of devices for the computation.
    computation_shape: An optional list of `ints`. Defaults to `[]`.
      DEPRECATED. Use num_cores_per_replica instead.
    host_compute_core: An optional list of `strings`. Defaults to `[]`.
    padding_map: An optional list of `strings`. Defaults to `[]`.
    step_marker_location: An optional `string`. Defaults to `"STEP_MARK_AT_ENTRY"`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUReplicateMetadata", name, _ctx._post_execution_callbacks,
        "num_replicas", num_replicas, "num_cores_per_replica",
        num_cores_per_replica, "topology", topology, "use_tpu", use_tpu,
        "device_assignment", device_assignment, "computation_shape",
        computation_shape, "host_compute_core", host_compute_core,
        "padding_map", padding_map, "step_marker_location",
        step_marker_location)
      return _result
    except _core._FallbackException:
      try:
        return tpu_replicate_metadata_eager_fallback(
            num_replicas=num_replicas,
            num_cores_per_replica=num_cores_per_replica, topology=topology,
            use_tpu=use_tpu, device_assignment=device_assignment,
            computation_shape=computation_shape,
            host_compute_core=host_compute_core, padding_map=padding_map,
            step_marker_location=step_marker_location, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_replicas = _execute.make_int(num_replicas, "num_replicas")
  if num_cores_per_replica is None:
    num_cores_per_replica = 1
  num_cores_per_replica = _execute.make_int(num_cores_per_replica, "num_cores_per_replica")
  if topology is None:
    topology = ""
  topology = _execute.make_str(topology, "topology")
  if use_tpu is None:
    use_tpu = True
  use_tpu = _execute.make_bool(use_tpu, "use_tpu")
  if device_assignment is None:
    device_assignment = []
  if not isinstance(device_assignment, (list, tuple)):
    raise TypeError(
        "Expected list for 'device_assignment' argument to "
        "'tpu_replicate_metadata' Op, not %r." % device_assignment)
  device_assignment = [_execute.make_int(_i, "device_assignment") for _i in device_assignment]
  if computation_shape is None:
    computation_shape = []
  if not isinstance(computation_shape, (list, tuple)):
    raise TypeError(
        "Expected list for 'computation_shape' argument to "
        "'tpu_replicate_metadata' Op, not %r." % computation_shape)
  computation_shape = [_execute.make_int(_i, "computation_shape") for _i in computation_shape]
  if host_compute_core is None:
    host_compute_core = []
  if not isinstance(host_compute_core, (list, tuple)):
    raise TypeError(
        "Expected list for 'host_compute_core' argument to "
        "'tpu_replicate_metadata' Op, not %r." % host_compute_core)
  host_compute_core = [_execute.make_str(_s, "host_compute_core") for _s in host_compute_core]
  if padding_map is None:
    padding_map = []
  if not isinstance(padding_map, (list, tuple)):
    raise TypeError(
        "Expected list for 'padding_map' argument to "
        "'tpu_replicate_metadata' Op, not %r." % padding_map)
  padding_map = [_execute.make_str(_s, "padding_map") for _s in padding_map]
  if step_marker_location is None:
    step_marker_location = "STEP_MARK_AT_ENTRY"
  step_marker_location = _execute.make_str(step_marker_location, "step_marker_location")
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUReplicateMetadata", num_replicas=num_replicas,
                                num_cores_per_replica=num_cores_per_replica,
                                topology=topology, use_tpu=use_tpu,
                                device_assignment=device_assignment,
                                computation_shape=computation_shape,
                                host_compute_core=host_compute_core,
                                padding_map=padding_map,
                                step_marker_location=step_marker_location,
                                name=name)
  return _op
  _result = None
  return _result

def TPUReplicateMetadata(num_replicas, num_cores_per_replica=1, topology="", use_tpu=True, device_assignment=[], computation_shape=[], host_compute_core=[], padding_map=[], step_marker_location="STEP_MARK_AT_ENTRY", name=None):
  return tpu_replicate_metadata(num_replicas=num_replicas, num_cores_per_replica=num_cores_per_replica, topology=topology, use_tpu=use_tpu, device_assignment=device_assignment, computation_shape=computation_shape, host_compute_core=host_compute_core, padding_map=padding_map, step_marker_location=step_marker_location, name=name)
TPUReplicateMetadata.__doc__ = tpu_replicate_metadata.__doc__
TPUReplicateMetadata = _doc_controls.do_not_generate_docs(_kwarg_only(TPUReplicateMetadata))
tf_export("raw_ops.TPUReplicateMetadata")(TPUReplicateMetadata)


def tpu_replicate_metadata_eager_fallback(num_replicas, num_cores_per_replica=1, topology="", use_tpu=True, device_assignment=[], computation_shape=[], host_compute_core=[], padding_map=[], step_marker_location="STEP_MARK_AT_ENTRY", name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tpu_replicate_metadata
  """
  _ctx = ctx if ctx else _context.context()
  num_replicas = _execute.make_int(num_replicas, "num_replicas")
  if num_cores_per_replica is None:
    num_cores_per_replica = 1
  num_cores_per_replica = _execute.make_int(num_cores_per_replica, "num_cores_per_replica")
  if topology is None:
    topology = ""
  topology = _execute.make_str(topology, "topology")
  if use_tpu is None:
    use_tpu = True
  use_tpu = _execute.make_bool(use_tpu, "use_tpu")
  if device_assignment is None:
    device_assignment = []
  if not isinstance(device_assignment, (list, tuple)):
    raise TypeError(
        "Expected list for 'device_assignment' argument to "
        "'tpu_replicate_metadata' Op, not %r." % device_assignment)
  device_assignment = [_execute.make_int(_i, "device_assignment") for _i in device_assignment]
  if computation_shape is None:
    computation_shape = []
  if not isinstance(computation_shape, (list, tuple)):
    raise TypeError(
        "Expected list for 'computation_shape' argument to "
        "'tpu_replicate_metadata' Op, not %r." % computation_shape)
  computation_shape = [_execute.make_int(_i, "computation_shape") for _i in computation_shape]
  if host_compute_core is None:
    host_compute_core = []
  if not isinstance(host_compute_core, (list, tuple)):
    raise TypeError(
        "Expected list for 'host_compute_core' argument to "
        "'tpu_replicate_metadata' Op, not %r." % host_compute_core)
  host_compute_core = [_execute.make_str(_s, "host_compute_core") for _s in host_compute_core]
  if padding_map is None:
    padding_map = []
  if not isinstance(padding_map, (list, tuple)):
    raise TypeError(
        "Expected list for 'padding_map' argument to "
        "'tpu_replicate_metadata' Op, not %r." % padding_map)
  padding_map = [_execute.make_str(_s, "padding_map") for _s in padding_map]
  if step_marker_location is None:
    step_marker_location = "STEP_MARK_AT_ENTRY"
  step_marker_location = _execute.make_str(step_marker_location, "step_marker_location")
  _inputs_flat = []
  _attrs = ("num_replicas", num_replicas, "num_cores_per_replica",
  num_cores_per_replica, "topology", topology, "use_tpu", use_tpu,
  "device_assignment", device_assignment, "computation_shape",
  computation_shape, "host_compute_core", host_compute_core, "padding_map",
  padding_map, "step_marker_location", step_marker_location)
  _result = _execute.execute(b"TPUReplicateMetadata", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def tpu_replicated_input(inputs, name=None):
  r"""Connects N inputs to an N-way replicated TPU computation.

  Args:
    inputs: A list of at least 1 `Tensor` objects with the same type.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `inputs`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUReplicatedInput", name, _ctx._post_execution_callbacks, inputs)
      return _result
    except _core._FallbackException:
      try:
        return tpu_replicated_input_eager_fallback(
            inputs, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'tpu_replicated_input' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUReplicatedInput", inputs=inputs, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"))
  _execute.record_gradient(
      "TPUReplicatedInput", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def TPUReplicatedInput(inputs, name=None):
  return tpu_replicated_input(inputs=inputs, name=name)
TPUReplicatedInput.__doc__ = tpu_replicated_input.__doc__
TPUReplicatedInput = _doc_controls.do_not_generate_docs(_kwarg_only(TPUReplicatedInput))
tf_export("raw_ops.TPUReplicatedInput")(TPUReplicatedInput)


def tpu_replicated_input_eager_fallback(inputs, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tpu_replicated_input
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'tpu_replicated_input' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _attr_T, inputs = _execute.args_to_matching_eager(list(inputs), _ctx)
  _inputs_flat = list(inputs)
  _attrs = ("N", _attr_N, "T", _attr_T)
  _result = _execute.execute(b"TPUReplicatedInput", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "TPUReplicatedInput", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def tpu_replicated_output(input, num_replicas, name=None):
  r"""Connects outputs of an N-way replicated computation to N outputs.

  Args:
    input: A `Tensor`.
    num_replicas: An `int` that is `>= 1`.
    name: A name for the operation (optional).

  Returns:
    A list of `num_replicas` `Tensor` objects with the same type as `input`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "TPUReplicatedOutput", name, _ctx._post_execution_callbacks, input,
        "num_replicas", num_replicas)
      return _result
    except _core._FallbackException:
      try:
        return tpu_replicated_output_eager_fallback(
            input, num_replicas=num_replicas, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  num_replicas = _execute.make_int(num_replicas, "num_replicas")
  _, _, _op = _op_def_lib._apply_op_helper(
        "TPUReplicatedOutput", input=input, num_replicas=num_replicas,
                               name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("num_replicas", _op.get_attr("num_replicas"), "T",
            _op.get_attr("T"))
  _execute.record_gradient(
      "TPUReplicatedOutput", _inputs_flat, _attrs, _result, name)
  return _result

def TPUReplicatedOutput(input, num_replicas, name=None):
  return tpu_replicated_output(input=input, num_replicas=num_replicas, name=name)
TPUReplicatedOutput.__doc__ = tpu_replicated_output.__doc__
TPUReplicatedOutput = _doc_controls.do_not_generate_docs(_kwarg_only(TPUReplicatedOutput))
tf_export("raw_ops.TPUReplicatedOutput")(TPUReplicatedOutput)


def tpu_replicated_output_eager_fallback(input, num_replicas, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function tpu_replicated_output
  """
  _ctx = ctx if ctx else _context.context()
  num_replicas = _execute.make_int(num_replicas, "num_replicas")
  _attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
  _inputs_flat = [input]
  _attrs = ("num_replicas", num_replicas, "T", _attr_T)
  _result = _execute.execute(b"TPUReplicatedOutput", num_replicas,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "TPUReplicatedOutput", _inputs_flat, _attrs, _result, name)
  return _result


def worker_heartbeat(request, name=None):
  r"""Worker heartbeat op.

  Heartbeats may be sent periodically to indicate the coordinator is still active,
  to retrieve the current worker status and to expedite shutdown when necessary.

  Args:
    request: A `Tensor` of type `string`.
      A string tensor containing a serialized WorkerHeartbeatRequest
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "WorkerHeartbeat", name, _ctx._post_execution_callbacks, request)
      return _result
    except _core._FallbackException:
      try:
        return worker_heartbeat_eager_fallback(
            request, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    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)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "WorkerHeartbeat", request=request, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = None
  _execute.record_gradient(
      "WorkerHeartbeat", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def WorkerHeartbeat(request, name=None):
  return worker_heartbeat(request=request, name=name)
WorkerHeartbeat.__doc__ = worker_heartbeat.__doc__
WorkerHeartbeat = _doc_controls.do_not_generate_docs(_kwarg_only(WorkerHeartbeat))
tf_export("raw_ops.WorkerHeartbeat")(WorkerHeartbeat)


def worker_heartbeat_eager_fallback(request, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function worker_heartbeat
  """
  _ctx = ctx if ctx else _context.context()
  request = _ops.convert_to_tensor(request, _dtypes.string)
  _inputs_flat = [request]
  _attrs = None
  _result = _execute.execute(b"WorkerHeartbeat", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "WorkerHeartbeat", _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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InfeedEnqueuePrelinearizedBuffer\022\t\n\005input\030\025\"\"\n\016device_ordinal\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001\n\230\001\n\022InfeedEnqueueTuple\022\020\n\006inputs2\006dtypes\"\030\n\006dtypes\022\nlist(type)(\0010\001\"\025\n\006shapes\022\013list(shape)\"\030\n\007layouts\022\tlist(int)\032\002\n\000\"\"\n\016device_ordinal\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001\210\001\001\n\271\001\n\036LoadTPUEmbeddingADAMParameters\022\016\n\nparameters\030\001\022\013\n\007momenta\030\001\022\016\n\nvelocities\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\342\001\n,LoadTPUEmbeddingADAMParametersGradAccumDebug\022\016\n\nparameters\030\001\022\013\n\007momenta\030\001\022\016\n\nvelocities\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\277\001\n\"LoadTPUEmbeddingAdadeltaParameters\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\013\n\007updates\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\350\001\n0LoadTPUEmbeddingAdadeltaParametersGradAccumDebug\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\013\n\007updates\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\261\001\n!LoadTPUEmbeddingAdagradParameters\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\332\001\n/LoadTPUEmbeddingAdagradParametersGradAccumDebug\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\300\001\n)LoadTPUEmbeddingCenteredRMSPropParameters\022\016\n\nparameters\030\001\022\006\n\002ms\030\001\022\007\n\003mom\030\001\022\006\n\002mg\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\273\001\n\036LoadTPUEmbeddingFTRLParameters\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\013\n\007linears\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\344\001\n,LoadTPUEmbeddingFTRLParametersGradAccumDebug\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\013\n\007linears\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\324\001\n)LoadTPUEmbeddingMDLAdagradLightParameters\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\013\n\007weights\030\001\022\014\n\010benefits\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\255\001\n\"LoadTPUEmbeddingMomentumParameters\022\016\n\nparameters\030\001\022\013\n\007momenta\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\326\001\n0LoadTPUEmbeddingMomentumParametersGradAccumDebug\022\016\n\nparameters\030\001\022\013\n\007momenta\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\271\001\n)LoadTPUEmbeddingProximalAdagradParameters\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\342\001\n7LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug\022\016\n\nparameters\030\001\022\020\n\014accumulators\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\260\001\n!LoadTPUEmbeddingRMSPropParameters\022\016\n\nparameters\030\001\022\006\n\002ms\030\001\022\007\n\003mom\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\331\001\n/LoadTPUEmbeddingRMSPropParametersGradAccumDebug\022\016\n\nparameters\030\001\022\006\n\002ms\030\001\022\007\n\003mom\030\001\022\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\261\001\n3LoadTPUEmbeddingStochasticGradientDescentParameters\022\016\n\nparameters\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\ng\n\016OutfeedDequeue\032\017\n\006output\"\005dtype\"\r\n\005dtype\022\004type\"\016\n\005shape\022\005shape\"\"\n\016device_ordinal\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001\210\001\001\n\200\001\n\023OutfeedDequeueTuple\032\021\n\007outputs2\006dtypes\"\030\n\006dtypes\022\nlist(type)(\0010\001\"\025\n\006shapes\022\013list(shape)\"\"\n\016device_ordinal\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001\210\001\001\n2\n\016OutfeedEnqueue\022\016\n\005input\"\005dtype\"\r\n\005dtype\022\004type\210\001\001\nD\n\023OutfeedEnqueueTuple\022\020\n\006inputs2\006dtypes\"\030\n\006dtypes\022\nlist(type)(\0010\001\210\001\001\nf\n\014Prelinearize\022\016\n\005input\"\005dtype\032\n\n\006output\030\025\"\r\n\005dtype\022\004type\"\022\n\005shape\022\005shape\032\002:\000\"\027\n\006layout\022\tlist(int)\032\002\n\000\n|\n\021PrelinearizeTuple\022\020\n\006inputs2\006dtypes\032\n\n\006output\030\025\"\030\n\006dtypes\022\nlist(type)(\0010\001\"\025\n\006shapes\022\013list(shape)\"\030\n\007layouts\022\tlist(int)\032\002\n\000\nd\n\033RecvTPUEmbeddingActivations\032\030\n\007outputs\030\001*\013num_outputs\"\026\n\013num_outputs\022\003int(\0010\001\"\020\n\006config\022\006string\210\001\001\n\275\001\n\"RetrieveTPUEmbeddingADAMParameters\032\016\n\nparameters\030\001\032\013\n\007momenta\030\001\032\016\n\nvelocities\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\346\001\n0RetrieveTPUEmbeddingADAMParametersGradAccumDebug\032\016\n\nparameters\030\001\032\013\n\007momenta\030\001\032\016\n\nvelocities\030\001\032\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\303\001\n&RetrieveTPUEmbeddingAdadeltaParameters\032\016\n\nparameters\030\001\032\020\n\014accumulators\030\001\032\013\n\007updates\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\n\354\001\n4RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug\032\016\n\nparameters\030\001\032\020\n\014accumulators\030\001\032\013\n\007updates\030\001\032\031\n\025gradient_accumulators\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001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rd_id\022\003int\210\001\001\n\265\001\n7RetrieveTPUEmbeddingStochasticGradientDescentParameters\032\016\n\nparameters\030\001\")\n\010table_id\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001(\0010\377\377\377\377\377\377\377\377\377\001\"\030\n\ntable_name\022\006string\032\002\022\000\"\021\n\nnum_shards\022\003int\"\017\n\010shard_id\022\003int\210\001\001\nv\n\031SendTPUEmbeddingGradients\022\r\n\006inputs\030\001*\001N\022\026\n\016learning_rates\030\001*\002NN\"\014\n\001N\022\003int(\0010\001\"\017\n\002NN\022\003int\032\002\030\000(\001\"\020\n\006config\022\006string\210\001\001\n\033\n\026ShutdownDistributedTPU\210\001\001\n\"\n\024TPUCompilationResult\032\n\n\006output\030\007\n|\n\027TPUEmbeddingActivations\022\026\n\022embedding_variable\030\001\022\026\n\022sliced_activations\030\001\032\n\n\006output\030\001\"\021\n\010table_id\022\003int(\001\"\022\n\tlookup_id\022\003int(\001\n,\n\022TPUOrdinalSelector\032\023\n\017device_ordinals\030\003\210\001\001\n{\n\022TPUPartitionedCall\022\013\n\004args2\003Tin\022\022\n\016device_ordinal\030\003\032\016\n\006output2\004Tout\"\023\n\003Tin\022\nlist(type)(\001\"\024\n\004Tout\022\nlist(type)(\001\"\t\n\001f\022\004func\n\302\002\n\024TPUReplicateMetadata\"\025\n\014num_replicas\022\003int(\001\" \n\025num_cores_per_replica\022\003int\032\002\030\001\"\026\n\010topology\022\006string\032\002\022\000\"\023\n\007use_tpu\022\004bool\032\002(\001\"\"\n\021device_assignment\022\tlist(int)\032\002\n\000\"\"\n\021computation_shape\022\tlist(int)\032\002\n\000\"%\n\021host_compute_core\022\014list(string)\032\002\n\000\"\037\n\013padding_map\022\014list(string)\032\002\n\000\"4\n\024step_marker_location\022\006string\032\024\022\022STEP_MARK_AT_ENTRY\nJ\n\022TPUReplicatedInput\022\016\n\006inputs\"\001T*\001N\032\013\n\006output\"\001T\"\014\n\001N\022\003int(\0010\001\"\t\n\001T\022\004type\na\n\023TPUReplicatedOutput\022\n\n\005input\"\001T\032\032\n\007outputs\"\001T*\014num_replicas\"\027\n\014num_replicas\022\003int(\0010\001\"\t\n\001T\022\004type\n/\n\017WorkerHeartbeat\022\013\n\007request\030\007\032\014\n\010response\030\007\210\001\001")
