parameters: (self, *input_types, **kwargs)
docstring: Create a `Defun` decorator.
  
      Args:
        *input_types: A list of `tf.DType`
        **kwargs: Optional keyword arguments, including
           func_name - (optional).  A python string, the name to use to
             declare this `Function` in the graph.
  
           grad_func - (optional).  A function implementing the gradient
             of the function-to-register.  This is must be a
             `_DefinedFunction` object. The gradient
             function must satisfy the criterion defined in
             function.proto:GradientDef.
  
           python_grad_func - (optional).  A function implementing the
             gradient of the function python-side. This function must
             take the current op and the gradients w.r.t. its outputs,
             and return the gradients w.r.t. the inputs. That is it must
             implement the interface expected by `tf.RegisterGradient`).
             This will be called by tf.gradients to add the gradient ops
             to the graph. At most one of grad_func and python_grad_func
             can be specified.
  
           out_names = (optional). A list of strings, one per output
             tensor.
  
           shape_func - (optional). A function taking the op and returning a list
             of static shapes to set for the function's outputs.
docstring_summary: Create a `Defun` decorator.
docstring_tokens: Create a Defun decorator .
function: def __init__(self, *input_types, **kwargs):
      """Create a `Defun` decorator.
  
      Args:
        *input_types: A list of `tf.DType`
        **kwargs: Optional keyword arguments, including
           func_name - (optional).  A python string, the name to use to
             declare this `Function` in the graph.
  
           grad_func - (optional).  A function implementing the gradient
             of the function-to-register.  This is must be a
             `_DefinedFunction` object. The gradient
             function must satisfy the criterion defined in
             function.proto:GradientDef.
  
           python_grad_func - (optional).  A function implementing the
             gradient of the function python-side. This function must
             take the current op and the gradients w.r.t. its outputs,
             and return the gradients w.r.t. the inputs. That is it must
             implement the interface expected by `tf.RegisterGradient`).
             This will be called by tf.gradients to add the gradient ops
             to the graph. At most one of grad_func and python_grad_func
             can be specified.
  
           out_names = (optional). A list of strings, one per output
             tensor.
  
           shape_func - (optional). A function taking the op and returning a list
             of static shapes to set for the function's outputs.
      """
      self._input_types = input_types
      self._func_name = kwargs.pop("func_name", None)
      self._grad_func = kwargs.pop("grad_func", None)
      self._python_grad_func = kwargs.pop("python_grad_func", None)
      self._out_names = kwargs.pop("out_names", None)
      self._extra_kwargs = kwargs
function_tokens: def __init__ ( self , * input_types , * * kwargs ) : self . _input_types = input_types self . _func_name = kwargs . pop ( "func_name" , None ) self . _grad_func = kwargs . pop ( "grad_func" , None ) self . _python_grad_func = kwargs . pop ( "python_grad_func" , None ) self . _out_names = kwargs . pop ( "out_names" , None ) self . _extra_kwargs = kwargs