code: def get_features(request):
      if request.param == "feat_dict":
          return (
              hf_datasets.Features(
                  {
                      "id": hf_datasets.Value("string"),
                      "meta": {
                          "left_context": hf_datasets.Value("string"),
                          "partial_evidence": [
                              {
                                  "start_id": hf_datasets.Value(
                                      "int32"
                                  ),
                                  "meta": {
                                      "evidence_span": [
                                          hf_datasets.Value("string")
                                      ]
                                  },
                              }
                          ],
                      },
                  }
              ),
              feature_lib.FeaturesDict(
                  {
                      "id": feature_lib.Scalar(dtype=np.str_),
                      "meta": feature_lib.FeaturesDict(
                          {
                              "left_context": feature_lib.Scalar(
                                  dtype=np.str_
                              ),
                              "partial_evidence": feature_lib.Sequence(
                                  {
                                      "meta": feature_lib.FeaturesDict(
                                          {
                                              "evidence_span": feature_lib.Sequence(
                                                  feature_lib.Scalar(
                                                      dtype=np.str_
                                                  )
                                              ),
                                          }
                                      ),
                                      "start_id": feature_lib.Scalar(
                                          dtype=np.int32
                                      ),
                                  }
                              ),
                          }
                      ),
                  }
              ),
          )
      elif request.param == "audio":
          return (
              hf_datasets.Audio(sampling_rate=48000),
              feature_lib.Audio(sample_rate=48000),
          )
  
imports: import datetime
  from unittest import mock
  from absl import logging
  import numpy
  import pytest
  import tensorflow
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import lazy_imports_lib
  from tensorflow_datasets.core import registered
  from tensorflow_datasets.core.dataset_builders import huggingface_dataset_builder
label_desc: Use image node_id 13 for calling a global function with example usage: get_features(request) and returns: hf_datasets,  feature_lib, hf_datasets,  feature_lib