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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 pretokenized_tfrecord_dataset(filenames, text2self, eos_included, repeat, batch_size, sequence_length):
 """Reads tensor2tensor-style data files. The dataset is defined by sets of TFRecord files of TFExample protos. There should be a "targets" feature (a 1d tensor of integers) If not text2self, there should also be an "inputs" feature. Other features get ignored. eos_included specifies whether the inputs and targets were written with an EOS token, as in tensor2tensor Args: filenames: a list of strings text2self: a boolean eos_included: a boolean repeat: a boolean batch_size: an integer sequence_length: an integer Returns: A tf.data.Dataset of batches """

  dataset = tf.data.TFRecordDataset(filenames, buffer_size=64 * 1024 * 1024)
  if repeat:
    dataset = dataset.repeat()
  keys = ["targets"] if text2self else ["inputs", "targets"]
  def decode_example(serialized_example):
    """Return a dict of Tensors from a serialized tensorflow.Example."""
    data_fields = {}
    data_items_to_decoders = {}
    for k in keys:
      data_fields[k] = tf.VarLenFeature(tf.int64)
      data_items_to_decoders[k] = tf.contrib.slim.tfexample_decoder.Tensor(k)
    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)
    if not eos_included:
      decoded = [tf.concat([v, [1]], 0) for v in decoded]
    return dict(zip(decode_items, decoded))
  dataset = dataset.map(decode_example,
                        num_parallel_calls=tf.data.experimental.AUTOTUNE)
  return pack_and_batch(dataset, batch_size, sequence_length)