# Goals:
        # * Convert values to Tensors if it contains constants.
        # * Verify that values is a list if that matches the input_arg's
        #   type.
        # * If the input_arg's type is determined by attrs, either set
        #   those attrs and validate those attr values are legal (if
        #   they have not yet been set) or validate the input matches
        #   the type indicated by the attrs (if they have already been
        #   inferred via an earlier input).
        # * If the input_arg has an explicit type, make sure the input
        #   conforms.

        if _IsListParameter(input_arg):
          """
              (Skip line 408~470)
          """
          types = [x.dtype for x in values]
          inputs.extend(values)
        else:
          # In cases where we have an expected type, try to convert non-Tensor
          # arguments to that type.
          dtype = None
          default_dtype = None
          if input_arg.type != types_pb2.DT_INVALID:
            dtype = input_arg.type
          elif input_arg.type_attr in attrs:
            dtype = attrs[input_arg.type_attr]
          elif input_arg.type_attr in default_type_attr_map:
            # The dtype could not be inferred solely from the inputs,
            # so we prefer the attr's default, so code that adds a new attr
            # with a default is backwards compatible.
            default_dtype = default_type_attr_map[input_arg.type_attr]

          try:
            values = ops.internal_convert_to_tensor(
                values,
                name=input_arg.name,
                dtype=dtype,
                as_ref=input_arg.is_ref,
                preferred_dtype=default_dtype)
          except TypeError as err:
            """
              (Skip line 497~531)
            """

          types = [values.dtype]
          inputs.append(values)
        base_types = [x.base_dtype for x in types]