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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 layer_postprocess(layer_input, layer_output, hparams):
 """Apply layer postprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_postprocess_sequence layer_prepostprocess_dropout norm_type hidden_size norm_epsilon Args: layer_input: a Tensor layer_output: a Tensor hparams: a hyperparameters object. Returns: a Tensor """

  return layer_prepostprocess(
      layer_input,
      layer_output,
      sequence=hparams.layer_postprocess_sequence,
      dropout_rate=hparams.layer_prepostprocess_dropout,
      norm_type=hparams.norm_type,
      depth=None,
      epsilon=hparams.norm_epsilon,
      dropout_broadcast_dims=comma_separated_string_to_integer_list(
          getattr(hparams, "layer_prepostprocess_dropout_broadcast_dims", "")),
      default_name="layer_postprocess")