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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 preprocess(self, dataset, mode, hparams, interleave=True):
 """Runtime preprocessing on the whole dataset. Return a tf.data.Datset -- the preprocessed version of the given one. By default this function calls preprocess_example. Args: dataset: the Dataset of already decoded but not yet preprocessed features. mode: tf.estimator.ModeKeys hparams: HParams, model hyperparameters interleave: bool, whether to use parallel_interleave, which is faster but will alter the order of samples non-deterministically, or flat_map, which is slower but will preserve the sample order. Returns: a Dataset """

    def _preprocess(example):
      examples = self.preprocess_example(example, mode, hparams)
      if not isinstance(examples, tf.data.Dataset):
        examples = tf.data.Dataset.from_tensors(examples)
      return examples

    if interleave:
      dataset = dataset.apply(
          tf.data.experimental.parallel_interleave(
              _preprocess, sloppy=True, cycle_length=8))
    else:
      dataset = dataset.flat_map(_preprocess)

    return dataset