Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorflow/python/user_ops/ops/gen_user_ops.py
2023-06-19 00:49:18 +02:00

74 lines
2.2 KiB
Python

"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
"""
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 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()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "Fact", name)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return fact_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(
"Fact", name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ()
_inputs_flat = _op.inputs
_execute.record_gradient(
"Fact", _inputs_flat, _attrs, _result)
_result, = _result
return _result
Fact = tf_export("raw_ops.Fact")(_ops.to_raw_op(fact))
def fact_eager_fallback(name, ctx):
_inputs_flat = []
_attrs = None
_result = _execute.execute(b"Fact", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"Fact", _inputs_flat, _attrs, _result)
_result, = _result
return _result