class_docstr: The results from multiple TFMA runs, or a TFMA run on multiple models.
code: def __init__(
      self,
      results: List[EvalResult],
      mode: str = constants.UNKNOWN_EVAL_MODE,
  ):
      supported_modes = [
          constants.DATA_CENTRIC_MODE,
          constants.MODEL_CENTRIC_MODE,
      ]
      if mode not in supported_modes:
          raise ValueError(
              "Mode " + mode + " must be one of " + str(supported_modes)
          )
  
      self._results = results
      self._mode = mode
  
imports: import copy
  from typing import Any, Dict, List, Sequence, NamedTuple, Optional, Union
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
label_desc: Use image node_id 1 to create a new EvalResults object  with example: obj = EvalResults(results, mode)