(name=None, fn_kwargs=None)
return lambda ds, unused_training: f(ds)
Returns a closure of any T5 preprocessor function with its arguments.

  The main use-case is to use this (with gin scopes) to make any preprocessor
  function available in a gin file to configure and use.

  See: `TFInputs.test_gin_configurable_preprocessors`

  Args:
    name: str, name of the preprocessor function to configure.
    fn_kwargs: optional dictionary, the arguments to configure, these will be
      partially applied to the function given by `name`.

  Returns:
    a closure of the preprocessor function along with its arguments, this
    function takes two arguments only, dataset and boolean training and ignores
    the training and calls the t5 processor with the dataset (and closed over
    arguments only).
Returns a closure of any T5 preprocessor function with its arguments.
Returns a closure of any T5 preprocessor function with its arguments .
def get_t5_preprocessor_by_name(name=None, fn_kwargs=None):
  """Returns a closure of any T5 preprocessor function with its arguments.

  The main use-case is to use this (with gin scopes) to make any preprocessor
  function available in a gin file to configure and use.

  See: `TFInputs.test_gin_configurable_preprocessors`

  Args:
    name: str, name of the preprocessor function to configure.
    fn_kwargs: optional dictionary, the arguments to configure, these will be
      partially applied to the function given by `name`.

  Returns:
    a closure of the preprocessor function along with its arguments, this
    function takes two arguments only, dataset and boolean training and ignores
    the training and calls the t5 processor with the dataset (and closed over
    arguments only).
  """

  assert name is not None
  f = getattr(t5_data().preprocessors, name)
  if fn_kwargs is not None:
    f = functools.partial(f, **fn_kwargs)
  return lambda ds, unused_training: f(ds)
def get_t5_preprocessor_by_name ( name = None , fn_kwargs = None ) : assert name is not None f = getattr ( t5_data ( ) . preprocessors , name ) if fn_kwargs is not None : f = functools . partial ( f , * * fn_kwargs ) return lambda ds , unused_training : f ( ds )