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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def add_signature(name=None, inputs=None, outputs=None):
 """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. """

  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)