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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 serving_input_fn(self, hparams, decode_hparams=None, use_tpu=False):
 """Input fn for serving export, starting from serialized example."""

    mode = tf.estimator.ModeKeys.PREDICT
    serialized_example = tf.placeholder(
        dtype=tf.string, shape=[None], name="serialized_example")
    dataset = tf.data.Dataset.from_tensor_slices(serialized_example)
    dataset = dataset.map(self.decode_example)
    dataset = dataset.map(lambda ex: self.preprocess_example(ex, mode, hparams))
    dataset = dataset.map(data_reader.cast_ints_to_int32)

    if use_tpu:
      padded_shapes = data_reader.pad_for_tpu(dataset.output_shapes, hparams,
                                              hparams.max_length)
      batch_size = 1 if not decode_hparams else getattr(decode_hparams,
                                                        "batch_size", 1)
      dataset = dataset.padded_batch(
          batch_size, padded_shapes, drop_remainder=False)
      dataset = dataset.map(
          functools.partial(data_reader.pad_batch, batch_multiple=batch_size))
    else:
      dataset = dataset.padded_batch(
          tf.shape(serialized_example, out_type=tf.int64)[0],
          dataset.output_shapes)

    dataset = dataset.map(data_reader.standardize_shapes)
    features = tf.data.experimental.get_single_element(dataset)

    if self.has_inputs:
      features.pop("targets", None)

    return tf.estimator.export.ServingInputReceiver(
        features=features, receiver_tensors=serialized_example)