code: def get_tokenizer(
      config: ml_collections.ConfigDict,
  ) -> tokenizers.TextTokenizer:
      """Get tokenizer to decode strings for eval."""
      tokenizer_config = config.dataset_configs.get(
          "tokenizer", ml_collections.ConfigDict()
      )
      tokenizer_type = tokenizer_config.get("tokenizer_type", None)
      tokenizer_model = tokenizer_config.get("tokenizer_model", None)
  
      if tokenizer_type == "sentence_piece":
          if tokenizer_model is not None:
              tokenizer = t5_tokenizer.build_dmvr_sp_model(
                  tokenizer_model
              )
          else:
              tokenizer = t5_tokenizer.build_dmvr_sp_model()
      else:
          raise ValueError("Unsupported tokenizer.")
  
      return tokenizer
  
imports: import copy
  import dataclasses
  import functools
  import json
  import os
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from dmvr import tokenizers
  from flax import jax_utils
  from flax.core import unfreeze
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.t5 import model
  from scenic.projects.t5 import tokenizer
  from scenic.projects.vid2seq import load_utils
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import train_utils
  from scenic.projects.vid2seq import dvc_eval
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 11 for calling a global function with example usage: get_tokenizer(config) and returns: tokenizer