code: def _get_dataset(
      mixture_or_task_or_name, sequence_length, split, shuffle=True
  ):
      """Get a tf.data.Dataset for a given Task or Mixture.
  
      Args:
        mixture_or_task_or_name: Task or Mixture or str, the name of the Mixture or
          Task to train on or the Tasks or Mixture object itself.
          Must be pre-registered in the global `t5.data.TaskRegistry` or
          `t5.data.MixtureRegistry.`
        sequence_length: dict of int, a dict mapping feature name to length.
        split: str or `tensorflow_datasets.Split`, the data split to load.
        shuffle: boolean, whether to shuffle the dataset.
  
      Returns:
        A generator that produces batches of numpy examples.
      """
      if isinstance(mixture_or_task_or_name, str):
          task = seqio.get_mixture_or_task(mixture_or_task_or_name)
      else:
          task = mixture_or_task_or_name
  
      return task.get_dataset(sequence_length, split, shuffle=shuffle)
  
imports: import functools
  import itertools
  import os
  import re
  import time
  from absl import logging
  import mesh_tensorflow.transformer.dataset
  import seqio
  import t5.data
  from t5.models import utils
  from t5.models.t5_model import T5Model
  import tensorflow.compat.v1
  import tensorflow_datasets
  import torch
  import torch.utils.tensorboard
label_desc: Use image node_id 2 for calling a global function with example usage: _get_dataset(mixture_or_task_or_name, sequence_length, split, shuffle) and returns: task