code: def datatype_converter(
      field,
      int_dtype: Optional[type_utils.TfdsDType] = np.int64,
      float_dtype: Optional[type_utils.TfdsDType] = np.float32,
  ):
      """Converts a Croissant field to a TFDS-compatible feature.
  
      Args:
        field: A mlcroissant Field object.
        int_dtype: The dtype to use for TFDS integer features. Defaults to np.int64.
        float_dtype: The dtype to use for TFDS float features. Defaults to
          np.float32.
  
      Returns:
        Converted datatype for TFDS.
  
      Raises:
        NotImplementedError
      """
      if field.is_enumeration:
          raise NotImplementedError("Not implemented yet.")
  
      field_data_type = field.data_type
  
      if not field_data_type:
          return None
      elif field_data_type == int:
          return int_dtype
      elif field_data_type == float:
          return float_dtype
      elif field_data_type == bool:
          return np.bool_
      elif field_data_type == bytes:
          return text_feature.Text(doc=field.description)
      # We return a text feature for mlc.DataType.DATE features.
      elif field_data_type == pd.Timestamp:
          return text_feature.Text(doc=field.description)
      elif field_data_type == mlc.DataType.IMAGE_OBJECT:
          return image_feature.Image(doc=field.description)
      else:
          raise ValueError(f"Unknown data type: {field_data_type}.")
  
imports: from typing import Any, Dict, Optional, Sequence
  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 split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import features_dict
  from tensorflow_datasets.core.features import image_feature
  from tensorflow_datasets.core.features import sequence_feature
  from tensorflow_datasets.core.features import text_feature
  from tensorflow_datasets.core.utils import type_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import mlcroissant
  from tensorflow_datasets.core.utils.lazy_imports_utils import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: datatype_converter(field, int_dtype, float_dtype) and returns: None, int_dtype, float_dtype, np, text_feature, text_feature, image_feature