code: def supervised_to_dict(dataset, text2self):
    
    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)
docs: 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