code: def read_metrics_eval_result(
      metrics_artifact_uri: str,
      output_file_format: Optional[str] = None,
  ) -> Optional[tfma.EvalResult]:
      """Reads TFMA evaluation results from the evaluator output path.
  
      Args:
        metrics_artifact_uri: the output artifact path of a TFMA component.
        output_file_format: an optional file format of the payload.
  
      Returns:
        A TFMA EvalResults named tuple including configs and sliced metrics.
        Returns None if no slicing metrics found from `metrics_artifact_uri`.
      """
      result = tfma.load_eval_result(
          output_path=metrics_artifact_uri,
          output_file_format=output_file_format,
      )
      if not result.slicing_metrics:
          logging.warning(
              "Cannot load eval results from: %s", metrics_artifact_uri
          )
          return None
      return result
  
imports: import enum
  import os
  from typing import Any, Dict, Iterable, List, Optional, Sequence, Union
  import attr
  import ml_metadata
  import tensorflow
  import tensorflow_model_analysis
  from absl import logging
  from ml_metadata.proto import metadata_store_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
  from model_card_toolkit import model_card
label_desc: Use image node_id 11 for calling a global function with example usage: read_metrics_eval_result(metrics_artifact_uri, output_file_format) and returns: result, None