code: def custom_eval_shared_model(
      eval_saved_model_path: str,
      model_name: str,
      eval_config: tfma.EvalConfig,
      **kwargs: Dict[str, Any],
  ) -> tfma.EvalSharedModel:
      return tfma.default_eval_shared_model(
          eval_saved_model_path=eval_saved_model_path,
          model_name=model_name,
          eval_config=eval_config,
          add_metrics_callbacks=kwargs.get("add_metrics_callbacks"),
      )
  
imports: from typing import Any, Dict, List
  import tensorflow_model_analysis
  from tfx_bsl.tfxio import tensor_adapter
label_desc: Use image node_id 1 for calling a global function with example usage: custom_eval_shared_model(eval_saved_model_path, model_name, eval_config) and returns: tfma