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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docstr: Creates the graph for which we use to generate FPL and metrics.
  
  Create a pass-through graph which parses the input examples using the
  feature spec.
docstr_tok: ["Creates","the","graph","for","which","we","use","to","generate","FPL","and","metrics",".","Create","a","pass-through","graph","which","parses","the","input","examples","using","the","feature","spec","."]
code: def _create_graph(self):
      """Creates the graph for which we use to generate FPL and metrics.
  
      Create a pass-through graph which parses the input examples using the
      feature spec.
      """
      with self._graph.as_default():
          serialized_example = tf.compat.v1.placeholder(dtype=tf.string)
          features = tf.io.parse_example(
              serialized=serialized_example,
              features=self._config.feature_spec,
          )
          self._get_features_fn = self._session.make_callable(
              fetches=features, feed_list=[serialized_example]
          )
  
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 2 for calling the ModelAgnosticPredict obj's underlying member method code with example usage: obj._create_graph() without return types