code: def check_hf_encoder_requirements(
      config: "ModelConfig",
  ) -> None:  # noqa: F821
      """Checks that a HuggingFace encoder has a pretrained_model_name_or_path specified."""
  
      for input_feature in config.input_features:
          if input_feature.type == TEXT:
              if hasattr(input_feature.encoder, "use_pretrained"):
                  if (
                      input_feature.preprocessing.pretrained_model_name_or_path
                      is None
                  ):
                      raise ConfigValidationError(
                          "Pretrained model name or path must be specified for HuggingFace encoder."
                      )
  
imports: from abc import ABC, abstractmethod
  from re import findall
  from typing import Callable, TYPE_CHECKING
  from transformers import AutoConfig
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUDIO, BINARY, CATEGORY, IMAGE, IN_MEMORY, MIN_QUANTIZATION_BITS_FOR_MERGE_AND_UNLOAD, MODEL_ECD, MODEL_GBM, MODEL_LLM, NUMBER, SEQUENCE, SET, TEXT, TIMESERIES, VECTOR
  from ludwig.error import ConfigValidationError
  from ludwig.utils.metric_utils import get_feature_to_metric_names_map_from_feature_collection
  from ludwig.utils.misc_utils import merge_dict
label_desc: Use image node_id 16 for calling a global function with example usage: check_hf_encoder_requirements(config) without return types