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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 decode_example(self, serialized_example):
 """Return a dict of Tensors from a serialized tensorflow.Example."""

    data_fields, data_items_to_decoders = self.example_reading_spec()
    # Necessary to rejoin examples in the correct order with the Cloud ML Engine
    # batch prediction API.
    data_fields["batch_prediction_key"] = tf.FixedLenFeature([1], tf.int64, 0)
    if data_items_to_decoders is None:
      data_items_to_decoders = {
          field: tf.contrib.slim.tfexample_decoder.Tensor(field)
          for field in data_fields
      }

    decoder = tf.contrib.slim.tfexample_decoder.TFExampleDecoder(
        data_fields, data_items_to_decoders)

    decode_items = list(sorted(data_items_to_decoders))
    decoded = decoder.decode(serialized_example, items=decode_items)
    return dict(zip(decode_items, decoded))