human
I wrote the below code

    if (not isinstance(decoder, Decoder)):
        raise TypeError(('Expected    decoder    to    be    type    Decoder,    but    saw:    %s' % type(decoder)))
    with variable_scope.variable_scope((scope or 'decoder')) as varscope:
        if (varscope.caching_device is None):
            varscope.set_caching_device((lambda op: op.device))
        if (maximum_iterations is not None):
            maximum_iterations = ops.convert_to_tensor(maximum_iterations, dtype=dtypes.int32, name='maximum_iterations')
            if (maximum_iterations.get_shape().ndims != 0):
                raise ValueError('maximum_iterations    must    be    a    scalar')
        (initial_finished, initial_inputs, initial_state) = decoder.initialize()
        zero_outputs = _create_zero_outputs(decoder.output_size, decoder.output_dtype, decoder.batch_size)
        if (maximum_iterations is not None):
            initial_finished = math_ops.logical_or(initial_finished, (0 >= maximum_iterations))
        initial_time = constant_op.constant(0, dtype=dtypes.int32)
        def _shape(batch_size, from_shape):
            if (not isinstance(from_shape, tensor_shape.TensorShape)):
                return tensor_shape.TensorShape(None)
            else:
                batch_size = tensor_util.constant_value(ops.convert_to_tensor(batch_size, name='batch_size'))
                return tensor_shape.TensorShape([batch_size]).concatenate(from_shape)
        def _create_ta(s, d):
            return tensor_array_ops.TensorArray(dtype=d, size=0, dynamic_size=True, element_shape=_shape(decoder.batch_size, s))
        initial_outputs_ta = nest.map_structure(_create_ta, decoder.output_size, decoder.output_dtype)
        def condition(unused_time, unused_outputs_ta, unused_state, unused_inputs, finished):
            return math_ops.logical_not(math_ops.reduce_all(finished))
        def body(time, outputs_ta, state, inputs, finished):
            'Internal    while_loop    body.\n\n                        Args:\n                                time:   ...

What's a good function header?
 ... Here's a good function header?
assistant
#Here's a good function header.

def dynamic_decode(decoder, output_time_major=False, impute_finished=False, maximum_iterations=None, parallel_iterations=32, swap_memory=False, scope=None):