class_docstr: Abstraction for using a model agnostic evaluation.
  
  This class is an API interface to interact with the with Model Agnostic graph
  to do evaluation without needing an eval_saved_model.
  It serves two primary functions:
    1) Be able to generate an FPL given FPLs encoded in the tf.Examples input.
    2) Be able to do metric evaluations against the FPLs generated.
  
  Design Doc: go/model-agnostic-tfma
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code: def __init__(self, model_agnostic_config: ModelAgnosticConfig):
      self._graph = tf.Graph()
      self._session = tf.compat.v1.Session(graph=self._graph)
      self._config = model_agnostic_config
      try:
          self._create_graph()
      except (RuntimeError, ValueError) as exception:
          general_util.reraise_augmented(
              exception, "Failed to initialize agnostic model"
          )
  
file_path: model-analysis/tensorflow_model_analysis/model_agnostic_eval/model_agnostic_predict.py
imports: from typing import Any, Dict, List, NamedTuple
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import encoding
  from tensorflow_model_analysis.eval_saved_model import util
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 1 to create a new ModelAgnosticPredict object  with example: obj = ModelAgnosticPredict(model_agnostic_config)