code: def _convert_value(
      hf_value: Any, feature: feature_lib.FeatureConnector
  ) -> Any:
      """Converts a Huggingface value to a TFDS compatible value."""
      # See the docstring of _default_value for explanations.
      if hf_value is None:
          return _default_value(feature)
      if isinstance(hf_value, datetime.datetime):
          return int(hf_value.timestamp())
      elif isinstance(feature, feature_lib.ClassLabel):
          return hf_value
      elif isinstance(feature, feature_lib.Scalar):
          return hf_value
      elif isinstance(feature, feature_lib.Translation):
          if isinstance(hf_value, dict):
              # Replaces `None` values with the default value.
              return {
                  key: value
                  if value is not None
                  else _default_value(feature[key])
                  for key, value in hf_value.items()
              }
      elif isinstance(feature, feature_lib.FeaturesDict):
          if isinstance(hf_value, dict):
              return {
                  k: _convert_value(v, feature[k])
                  for k, v in hf_value.items()
              }
          raise ValueError(
              f"The feature is {feature}, but the value is: {hf_value}"
          )
      elif isinstance(feature, feature_lib.Sequence):
          if isinstance(hf_value, dict):
              # Should be a dict of lists:
              return {
                  k: [
                      _convert_value(el, feature.feature[k]) for el in v
                  ]
                  for k, v in hf_value.items()
              }
          if isinstance(hf_value, list):
              return [
                  _convert_value(v, feature.feature) for v in hf_value
              ]
          else:
              return [hf_value]
      elif isinstance(feature, feature_lib.Audio):
          assert isinstance(
              hf_value, dict
          ), f"Audio {hf_value} should be a dict"
          if "array" in hf_value:
              sample_rate = feature.sample_rate
              # Hugging Face uses float, TFDS uses integers.
              return [int(s * sample_rate) for s in hf_value["array"]]
          if "path" in hf_value:
              path = epath.Path(hf_value["path"])
              if path.exists():
                  return path
          else:
              raise ValueError(
                  f"{hf_value} is not a valid audio feature."
              )
      elif isinstance(
          hf_value, lazy_imports_lib.lazy_imports.PIL_Image.Image
      ):
          buffer = io.BytesIO()
          if hf_value.mode == "CMYK":
              # Convert CMYK images to RGB.
              hf_value = hf_value.convert("RGB")
          hf_value.save(fp=buffer, format=_IMAGE_ENCODING_FORMAT)
          return buffer.getvalue()
      elif isinstance(feature, feature_lib.Tensor):
          return hf_value
      raise ValueError(
          f"Type {type(hf_value)} of value {hf_value} "
          f"for feature {type(feature)} is not supported."
      )
  
imports: from __future__ import annotations
  import datetime
  import functools
  import io
  import itertools
  import multiprocessing
  import os
  from typing import Any, Dict, Mapping, Optional, Tuple, Type, Union
  from absl import logging
  from etils import epath
  import numpy
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_info
  from tensorflow_datasets.core import download
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import file_adapters
  from tensorflow_datasets.core import lazy_imports_lib
  from tensorflow_datasets.core import registered
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.utils import dtype_utils
  from tensorflow_datasets.core.utils import py_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: _convert_value(hf_value, feature) and returns: _default_value, int, hf_value, hf_value, unknown, unknown, unknown, unknown, list, unknown, buffer, path, hf_value