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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_t2t_dataset(dataset_name=gin.REQUIRED, text2self=False, data_dir=gin.REQUIRED, dataset_split="train", batch_size=gin.REQUIRED, sequence_length=gin.REQUIRED, vocabulary=None):
 """Loads the Tensor2tensor dataset specified by dataset_name. Args: dataset_name: TensorFlow Datasets dataset name. text2self: a boolean data_dir: string, data_dir for TensorFlow Datasets dataset_split: a string - "train" or "dev" batch_size: an integer sequence_length: an integer vocabulary: ignored Returns: A tf.data.Dataset of batches """

  del vocabulary
  filepattern = os.path.join(
      data_dir, dataset_name + "-" + dataset_split + "-*")
  filenames = tf.gfile.Glob(filepattern)
  tf.logging.info("Found %s files matching %s" % (len(filenames), filepattern))
  if not filenames:
    raise ValueError("No matching files found")
  dataset = pretokenized_tfrecord_dataset(
      filenames=filenames,
      text2self=text2self,
      eos_included=True,
      repeat=dataset_split == "train",
      batch_size=batch_size,
      sequence_length=sequence_length)
  if dataset_split == "train":
    dataset = dataset.shuffle(1000)
  return dataset