code: def custom_extractors(
      eval_shared_model: tfma.MaybeMultipleEvalSharedModels,
      eval_config: tfma.EvalConfig,
      tensor_adapter_config: tensor_adapter.TensorAdapterConfig,
  ) -> List[tfma.extractors.Extractor]:
      return tfma.default_extractors(
          eval_shared_model=eval_shared_model,
          eval_config=eval_config,
          tensor_adapter_config=tensor_adapter_config,
      )
  
imports: from typing import Any, Dict, List
  import tensorflow_model_analysis
  from tfx_bsl.tfxio import tensor_adapter
label_desc: Use image node_id 2 for calling a global function with example usage: custom_extractors(eval_shared_model, eval_config, tensor_adapter_config) and returns: tfma