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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 supervised_to_dict(dataset, text2self):
 """Turns a supervised dataset into a dataset with a feature dictionary. if text2self, then the features dictionary contains a "targets" key. else, the features dictionary contains "inputs" and "targets" keys. Args: dataset: a tf.data.Dataset text2self: a boolean Returns: a tf.data.Dataset """

  def my_fn(inputs, targets):
    if text2self:
      return {"targets": targets}
    else:
      return {"inputs": inputs, "targets": targets}
  return dataset.map(my_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)