code: def add_signature(name=None, inputs=None, outputs=None):
    
    if not name:
      name = "default"
    if inputs is None:
      inputs = {}
    if outputs is None:
      outputs = {}
    if not isinstance(inputs, dict):
      inputs = {"default": inputs}
    if not isinstance(outputs, dict):
      outputs = {"default": outputs}
    message = find_signature_inputs_from_multivalued_ops(inputs)
    if message: logging.error(message)
    message = find_signature_input_colocation_error(name, inputs)
    if message: raise ValueError(message)
    saved_model_lib.add_signature(name, inputs, outputs)
docs: Adds a signature to the module definition.
  
    NOTE: This must be called within a `module_fn` that is defining a Module.
  
    Args:
      name: Signature name as a string. If omitted, it is interpreted as 'default'
        and is the signature used when `Module.__call__` `signature` is not
        specified.
      inputs: A dict from input name to Tensor or SparseTensor to feed when
        applying the signature. If a single tensor is passed, it is interpreted
        as a dict with a single 'default' entry.
      outputs: A dict from output name to Tensor or SparseTensor to return from
        applying the signature. If a single tensor is passed, it is interpreted
        as a dict with a single 'default' entry.
  
    Raises:
      ValueError: if the arguments are invalid.