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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 add_weight(cls):
 """Decorator for Layers, overriding add_weight for trainable initializers."""

  @functools.wraps(cls.add_weight)
  def _add_weight(self,
                  name=None,
                  shape=None,
                  dtype=None,
                  initializer=None,
                  regularizer=None,
                  **kwargs):
    """Adds weight."""
    if isinstance(initializer, tf.keras.layers.Layer):
      weight = initializer(shape, dtype)
      self._trainable_weights.extend(initializer.trainable_weights)  # pylint: disable=protected-access
      self._non_trainable_weights.extend(initializer.non_trainable_weights)  # pylint: disable=protected-access
      if regularizer is not None:
        # TODO(trandustin): Replace need for this with
        # Layer._handle_weight_regularization. For Eager compatibility, random
        # variable __init__s cannot apply TF ops (cl/220898007).
        def loss_fn():
          """Creates a regularization loss `Tensor`."""
          with tf.name_scope(name + '/Regularizer'):
            return regularizer(initializer(shape, dtype))
        self.add_loss(loss_fn)
      return weight
    return super(cls, self).add_weight(name=name,
                                       shape=shape,
                                       dtype=dtype,
                                       initializer=initializer,
                                       regularizer=regularizer,
                                       **kwargs)
  cls.add_weight = _add_weight
  return cls