original_docstring: r"""  This op is used as a placeholder in If branch functions. It doesn't provide a
    valid output when run, so must either be removed (e.g. replaced with a
    function input) or guaranteed not to be used (e.g. if mirroring an
    intermediate output needed for the gradient computation of the other branch).
  
    Args:
      dtype: A `tf.DType`. The type of the output.
      shape: A `tf.TensorShape` or list of `ints`.
            The purported shape of the output. This is only used for shape inference;
            the output will not necessarily have this shape. Can be a partial shape.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `dtype`.
    
docstring: r"""  This op is used as a placeholder in If branch functions. It doesn't provide a
  valid output when run, so must either be removed  or guaranteed not to be used .
docstring_tokens: r " " " This op is used as a placeholder in If branch functions . It doesn ' t provide a valid output when run so must either be removed or guaranteed not to be used .
code: def fake_param(dtype, shape, name=None):
    r"""  This op is used as a placeholder in If branch functions. It doesn't provide a
    valid output when run, so must either be removed (e.g. replaced with a
    function input) or guaranteed not to be used (e.g. if mirroring an
    intermediate output needed for the gradient computation of the other branch).
  
    Args:
      dtype: A `tf.DType`. The type of the output.
      shape: A `tf.TensorShape` or list of `ints`.
            The purported shape of the output. This is only used for shape inference;
            the output will not necessarily have this shape. Can be a partial shape.
      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,
          "FakeParam", name, _ctx._post_execution_callbacks, "dtype", dtype,
          "shape", shape)
        return _result
      except _core._FallbackException:
        try:
          return fake_param_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(
          "FakeParam", 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(
        "FakeParam", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
code_tokens: def fake_param ( dtype , shape , name = None ) : _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 , "FakeParam" , name , _ctx . _post_execution_callbacks , "dtype" , dtype , "shape" , shape ) return _result except _core . _FallbackException : try : return fake_param_eager_fallback ( dtype = dtype , shape = shape , name = name , ctx = _ctx ) except _core . _SymbolicException : pass 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 ) dtype = _execute . make_type ( dtype , "dtype" ) shape = _execute . make_shape ( shape , "shape" ) _ , _ , _op = _op_def_lib . _apply_op_helper ( "FakeParam" , 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 ( "FakeParam" , _inputs_flat , _attrs , _result , name ) _result , = _result return _result
short_docstring: r"""  This op is used as a placeholder in If branch functions.
short_docstring_tokens: r " " " This op is used as a placeholder in If branch functions .
comment: r"""  This op is used as a placeholder in If branch functions. It doesn't provide a
    valid output when run, so must either be removed (e.g. replaced with a
    function input) or guaranteed not to be used (e.g. if mirroring an
    intermediate output needed for the gradient computation of the other branch).
  
    Args:
      dtype: A `tf.DType`. The type of the output.
      shape: A `tf.TensorShape` or list of `ints`.
            The purported shape of the output. This is only used for shape inference;
            the output will not necessarily have this shape. Can be a partial shape.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `dtype`.
    """$SEP$# Add nodes to the TensorFlow graph.$SEP$# Add nodes to the TensorFlow graph.