How to use the t5.data.preprocessors function in t5

elif builder_config.name in ["axb", "axg"]:
      # Cast the SuperGLUE diagnostic tasks as RTE.
      benchmark_name = "rte"
    else:
      benchmark_name = builder_config.name
    if builder_config.name == "multirc":
      feature_names = ("question", "answer", "paragraph")
    elif builder_config.name == "wic":
      # This ignores the start/end indices which show where in each sentence the
      # word appears.
      # TODO(craffel): Investigate using those indices.
      feature_names = ("sentence1", "sentence2", "word")
    else:
      feature_names = None
    return functools.partial(
        preprocessors.glue,
        benchmark_name=benchmark_name,
        label_names=builder_config.label_classes,
        feature_names=feature_names)

from t5.evaluation import metrics
import tensorflow_datasets as tfds




# ==================================== C4 ======================================
_c4_config_suffixes = ["", ".noclean", ".realnewslike", ".webtextlike"]
for config_suffix in _c4_config_suffixes:
  TaskRegistry.add(
      "c4{name}_v020_unsupervised".format(
          name=config_suffix.replace(".", "_")),
      TfdsTask,
      tfds_name="c4/en{config}:1.0.0".format(config=config_suffix),
      text_preprocessor=functools.partial(
          preprocessors.rekey, key_map={"inputs": None, "targets": "text"}),
      token_preprocessor=preprocessors.unsupervised,
      sentencepiece_model_path=DEFAULT_SPM_PATH,
      metric_fns=[])

# ================================ Wikipedia ===================================
TaskRegistry.add(
    "wikipedia_20190301.en_v003_unsupervised",
    TfdsTask,
    # 0.0.4 is identical to 0.0.3 except empty records removed.
    tfds_name="wikipedia/20190301.en:0.0.4",
    text_preprocessor=functools.partial(
        preprocessors.rekey, key_map={"inputs": None, "targets": "text"}),
    token_preprocessor=preprocessors.unsupervised,
    sentencepiece_model_path=DEFAULT_SPM_PATH,
    metric_fns=[])