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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_preprocess(layer_input, hparams, layer_collection=None):
 """Apply layer preprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_preprocess_sequence layer_prepostprocess_dropout norm_type hidden_size norm_epsilon Args: layer_input: a Tensor hparams: a hyperparameters object. layer_collection: A tensorflow_kfac.LayerCollection. Only used by the KFAC optimizer. Default is None. Returns: a Tensor """

  assert "a" not in hparams.layer_preprocess_sequence, (
      "No residual connections allowed in hparams.layer_preprocess_sequence")
  assert "z" not in hparams.layer_preprocess_sequence, (
      "No residual connections allowed in hparams.layer_preprocess_sequence")
  return layer_prepostprocess(
      None,
      layer_input,
      sequence=hparams.layer_preprocess_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_prepostprocess",
      layer_collection=layer_collection)