"""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 fact(name=None):
  r"""Output a fact about factorials.

  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, "Fact",
        name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return fact_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(
        "Fact", name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = None
  _execute.record_gradient(
      "Fact", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

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


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

_ops.RegisterShape("Fact")(None)

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
# op {
#   name: "Fact"
#   output_arg {
#     name: "fact"
#     type: DT_STRING
#   }
# }
_op_def_lib = _InitOpDefLibrary(b"\n\020\n\004Fact\032\010\n\004fact\030\007")