code: def metric_computations_using_eval_saved_model(
      model_name: str,
      model_loader: types.ModelLoader,
      batch_size: Optional[int] = None,
  ) -> metric_types.MetricComputations:
      """Returns computations for computing metrics natively using EvalMetricsGraph.
  
      Note that unlike other computations, there is no direct key associated with
      this computation. Instead the final computation returns the actual internal
      metric keys used by the model such as 'auc', etc).
  
      Args:
        model_name: Name of model.
        model_loader: Loader for shared model containing eval saved model to use for
          metric computations.
        batch_size: Batch size to use during evaluation (testing only).
      """
      return [
          metric_types.MetricComputation(
              keys=[],
              preprocessors=[metric_types.InputPreprocessor()],
              combiner=_EvalSavedModelCombiner(
                  model_name, model_loader, batch_size
              ),
          )
      ]
  
imports: from typing import Any, Dict, Iterable, List, Optional
  import apache_beam
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_model_analysis.utils import size_estimator
label_desc: Use image node_id 1 for calling a global function with example usage: metric_computations_using_eval_saved_model(model_name, model_loader, batch_size) and returns: list