EVOLUTION-MANAGER

Edit File: gen_control_flow_ops.py

"""Python wrappers around TensorFlow ops.

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

import collections

from tensorflow.python import pywrap_tfe as pywrap_tfe
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 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 typing import TypeVar

def abort(error_msg="", exit_without_error=False, name=None):
  r"""Raise a exception to abort the process when called.

If exit_without_error is true, the process will exit normally,
  otherwise it will exit with a SIGABORT signal.

Returns nothing but an exception.

Args:
    error_msg: An optional `string`. Defaults to `""`.
      A string which is the message associated with the exception.
    exit_without_error: An optional `bool`. Defaults to `False`.
    name: A name for the operation (optional).

Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "Abort", name, "error_msg", error_msg, "exit_without_error",
        exit_without_error)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return abort_eager_fallback(
          error_msg=error_msg, exit_without_error=exit_without_error,
          name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  if error_msg is None:
    error_msg = ""
  error_msg = _execute.make_str(error_msg, "error_msg")
  if exit_without_error is None:
    exit_without_error = False
  exit_without_error = _execute.make_bool(exit_without_error, "exit_without_error")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "Abort", error_msg=error_msg, exit_without_error=exit_without_error,
                 name=name)
  return _op
Abort = tf_export("raw_ops.Abort")(_ops.to_raw_op(abort))

def abort_eager_fallback(error_msg, exit_without_error, name, ctx):
  if error_msg is None:
    error_msg = ""
  error_msg = _execute.make_str(error_msg, "error_msg")
  if exit_without_error is None:
    exit_without_error = False
  exit_without_error = _execute.make_bool(exit_without_error, "exit_without_error")
  _inputs_flat = []
  _attrs = ("error_msg", error_msg, "exit_without_error", exit_without_error)
  _result = _execute.execute(b"Abort", 0, inputs=_inputs_flat, attrs=_attrs,
                             ctx=ctx, name=name)
  _result = None
  return _result

def control_trigger(name=None):
  r"""Does nothing. Serves as a control trigger for scheduling.

Only useful as a placeholder for control edges.

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

Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "ControlTrigger", name)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return control_trigger_eager_fallback(
          name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "ControlTrigger", name=name)
  return _op
ControlTrigger = tf_export("raw_ops.ControlTrigger")(_ops.to_raw_op(control_trigger))

def control_trigger_eager_fallback(name, ctx):
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"ControlTrigger", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  _result = None
  return _result

def enter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  r"""Creates or finds a child frame, and makes `data` available to the child frame.

This op is used together with `Exit` to create loops in the graph.
  The unique `frame_name` is used by the `Executor` to identify frames. If
  `is_constant` is true, `output` is a constant in the child frame; otherwise
  it may be changed in the child frame. At most `parallel_iterations` iterations
  are run in parallel in the child frame.

Args:
    data: A `Tensor`. The tensor to be made available to the child frame.
    frame_name: A `string`. The name of the child frame.
    is_constant: An optional `bool`. Defaults to `False`.
      If true, the output is constant within the child frame.
    parallel_iterations: An optional `int`. Defaults to `10`.
      The number of iterations allowed to run in parallel.
    name: A name for the operation (optional).

Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "Enter", name, data, "frame_name", frame_name, "is_constant",
        is_constant, "parallel_iterations", parallel_iterations)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return enter_eager_fallback(
          data, frame_name=frame_name, is_constant=is_constant,
          parallel_iterations=parallel_iterations, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "Enter", data=data, frame_name=frame_name, is_constant=is_constant,
                 parallel_iterations=parallel_iterations, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"), "frame_name",
              _op.get_attr("frame_name"), "is_constant",
              _op._get_attr_bool("is_constant"), "parallel_iterations",
              _op._get_attr_int("parallel_iterations"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "Enter", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

Enter = tf_export("raw_ops.Enter")(_ops.to_raw_op(enter))

def enter_eager_fallback(data, frame_name, is_constant, parallel_iterations, name, ctx):
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx, [])
  _inputs_flat = [data]
  _attrs = ("T", _attr_T, "frame_name", frame_name, "is_constant",
  is_constant, "parallel_iterations", parallel_iterations)
  _result = _execute.execute(b"Enter", 1, inputs=_inputs_flat, attrs=_attrs,
                             ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "Enter", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

def _exit(data, name=None):
  r"""Exits the current frame to its parent frame.

Exit makes its input `data` available to the parent frame.

Args:
    data: A `Tensor`. The tensor to be made available to the parent frame.
    name: A name for the operation (optional).

Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "Exit", name, data)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return _exit_eager_fallback(
          data, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "Exit", data=data, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "Exit", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

Exit = tf_export("raw_ops.Exit")(_ops.to_raw_op(_exit))

def _exit_eager_fallback(data, name, ctx):
  _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx, [])
  _inputs_flat = [data]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"Exit", 1, inputs=_inputs_flat, attrs=_attrs,
                             ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "Exit", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

def loop_cond(input, name=None):
  r"""Forwards the input to the output.

This operator represents the loop termination condition used by the
  "pivot" switches of a loop.

Args:
    input: A `Tensor` of type `bool`.
      A boolean scalar, representing the branch predicate of the Switch op.
    name: A name for the operation (optional).

Returns:
    A `Tensor` of type `bool`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "LoopCond", name, input)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return loop_cond_eager_fallback(
          input, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "LoopCond", input=input, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ()
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "LoopCond", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

LoopCond = tf_export("raw_ops.LoopCond")(_ops.to_raw_op(loop_cond))

def loop_cond_eager_fallback(input, name, ctx):
  input = _ops.convert_to_tensor(input, _dtypes.bool)
  _inputs_flat = [input]
  _attrs = None
  _result = _execute.execute(b"LoopCond", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "LoopCond", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

_MergeOutput = collections.namedtuple(
    "Merge",
    ["output", "value_index"])

def merge(inputs, name=None):
  r"""Forwards the value of an available tensor from `inputs` to `output`.

`Merge` waits for at least one of the tensors in `inputs` to become available.
  It is usually combined with `Switch` to implement branching.

`Merge` forwards the first tensor to become available to `output`, and sets
  `value_index` to its index in `inputs`.

Args:
    inputs: A list of at least 1 `Tensor` objects with the same type.
      The input tensors, exactly one of which will become available.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (output, value_index).

output: A `Tensor`. Has the same type as `inputs`.
    value_index: A `Tensor` of type `int32`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "Merge", name, inputs)
      _result = _MergeOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return merge_eager_fallback(
          inputs, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "Merge", inputs=inputs, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"), "N", _op._get_attr_int("N"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "Merge", _inputs_flat, _attrs, _result)
  _result = _MergeOutput._make(_result)
  return _result

Merge = tf_export("raw_ops.Merge")(_ops.to_raw_op(merge))

def merge_eager_fallback(inputs, name, ctx):
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _attr_T, inputs = _execute.args_to_matching_eager(list(inputs), ctx, [])
  _inputs_flat = list(inputs)
  _attrs = ("T", _attr_T, "N", _attr_N)
  _result = _execute.execute(b"Merge", 2, inputs=_inputs_flat, attrs=_attrs,
                             ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "Merge", _inputs_flat, _attrs, _result)
  _result = _MergeOutput._make(_result)
  return _result

def next_iteration(data, name=None):
  r"""Makes its input available to the next iteration.

Args:
    data: A `Tensor`. The tensor to be made available to the next iteration.
    name: A name for the operation (optional).

Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "NextIteration", name, data)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return next_iteration_eager_fallback(
          data, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "NextIteration", data=data, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "NextIteration", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

NextIteration = tf_export("raw_ops.NextIteration")(_ops.to_raw_op(next_iteration))

def next_iteration_eager_fallback(data, name, ctx):
  _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx, [])
  _inputs_flat = [data]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"NextIteration", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "NextIteration", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

@_dispatch.add_dispatch_list
@tf_export('no_op')
def no_op(name=None):
  r"""Does nothing. Only useful as a placeholder for control edges.

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

Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "NoOp", name)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return no_op_eager_fallback(
          name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            no_op, (), dict(name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "NoOp", name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          no_op, (), dict(name=name)
        )
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  return _op
NoOp = tf_export("raw_ops.NoOp")(_ops.to_raw_op(no_op))

def no_op_eager_fallback(name, ctx):
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"NoOp", 0, inputs=_inputs_flat, attrs=_attrs,
                             ctx=ctx, name=name)
  _result = None
  return _result

def ref_enter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  r"""Creates or finds a child frame, and makes `data` available to the child frame.

The unique `frame_name` is used by the `Executor` to identify frames. If
  `is_constant` is true, `output` is a constant in the child frame; otherwise
  it may be changed in the child frame. At most `parallel_iterations` iterations
  are run in parallel in the child frame.

Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the child frame.
    frame_name: A `string`. The name of the child frame.
    is_constant: An optional `bool`. Defaults to `False`.
      If true, the output is constant within the child frame.
    parallel_iterations: An optional `int`. Defaults to `10`.
      The number of iterations allowed to run in parallel.
    name: A name for the operation (optional).

Returns:
    A mutable `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    raise RuntimeError("ref_enter op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "RefEnter", data=data, frame_name=frame_name, is_constant=is_constant,
                    parallel_iterations=parallel_iterations, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"), "frame_name",
              _op.get_attr("frame_name"), "is_constant",
              _op._get_attr_bool("is_constant"), "parallel_iterations",
              _op._get_attr_int("parallel_iterations"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "RefEnter", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

RefEnter = tf_export("raw_ops.RefEnter")(_ops.to_raw_op(ref_enter))

def ref_enter_eager_fallback(data, frame_name, is_constant, parallel_iterations, name, ctx):
  raise RuntimeError("ref_enter op does not support eager execution. Arg 'output' is a ref.")

def ref_exit(data, name=None):
  r"""Exits the current frame to its parent frame.

Exit makes its input `data` available to the parent frame.

Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the parent frame.
    name: A name for the operation (optional).

RefExit = tf_export("raw_ops.RefExit")(_ops.to_raw_op(ref_exit))

def ref_exit_eager_fallback(data, name, ctx):
  raise RuntimeError("ref_exit op does not support eager execution. Arg 'output' is a ref.")
_RefMergeOutput = collections.namedtuple(
    "RefMerge",
    ["output", "value_index"])

def ref_merge(inputs, name=None):
  r"""Forwards the value of an available tensor from `inputs` to `output`.

`Merge` waits for at least one of the tensors in `inputs` to become available.
  It is usually combined with `Switch` to implement branching.

`Merge` forwards the first tensor for become available to `output`, and sets
  `value_index` to its index in `inputs`.

Args:
    inputs: A list of at least 1 mutable `Tensor` objects with the same type.
      The input tensors, exactly one of which will become available.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (output, value_index).

output: A mutable `Tensor`. Has the same type as `inputs`.
    value_index: A `Tensor` of type `int32`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    raise RuntimeError("ref_merge op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'ref_merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "RefMerge", inputs=inputs, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"), "N", _op._get_attr_int("N"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "RefMerge", _inputs_flat, _attrs, _result)
  _result = _RefMergeOutput._make(_result)
  return _result

RefMerge = tf_export("raw_ops.RefMerge")(_ops.to_raw_op(ref_merge))

def ref_merge_eager_fallback(inputs, name, ctx):
  raise RuntimeError("ref_merge op does not support eager execution. Arg 'output' is a ref.")

def ref_next_iteration(data, name=None):
  r"""Makes its input available to the next iteration.

Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the next iteration.
    name: A name for the operation (optional).

RefNextIteration = tf_export("raw_ops.RefNextIteration")(_ops.to_raw_op(ref_next_iteration))

def ref_next_iteration_eager_fallback(data, name, ctx):
  raise RuntimeError("ref_next_iteration op does not support eager execution. Arg 'output' is a ref.")

def ref_select(index, inputs, name=None):
  r"""Forwards the `index`th element of `inputs` to `output`.

Args:
    index: A `Tensor` of type `int32`.
      A scalar that determines the input that gets selected.
    inputs: A list of at least 1 mutable `Tensor` objects with the same type.
      A list of ref tensors, one of which will be forwarded to `output`.
    name: A name for the operation (optional).

Returns:
    A mutable `Tensor`. Has the same type as `inputs`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    raise RuntimeError("ref_select op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'ref_select' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "RefSelect", index=index, inputs=inputs, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"), "N", _op._get_attr_int("N"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "RefSelect", _inputs_flat, _attrs, _result)
  _result, = _result
  return _result

RefSelect = tf_export("raw_ops.RefSelect")(_ops.to_raw_op(ref_select))

def ref_select_eager_fallback(index, inputs, name, ctx):
  raise RuntimeError("ref_select op does not support eager execution. Arg 'output' is a ref.")
_RefSwitchOutput = collections.namedtuple(
    "RefSwitch",
    ["output_false", "output_true"])

def ref_switch(data, pred, name=None):
  r"""Forwards the ref tensor `data` to the output port determined by `pred`.

If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
  the data goes to `output_false`.