class_docstr: Abstraction for a graph that is used for computing and aggregating metrics.
  
  This abstract class contains methods and lays out the API to handle metrics
  computation and aggregation as part of the TFMA flow. Inheritors of this class
  are responsible for setting up the metrics graph and setting the class
  variables which are required to do metric calculations.
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docstr: Abstract function that is responsible for graph construction.
  
  This method is called as part of init. Subclasses are also responsible for
  populating the variables in the __init__ method as part of graph
  construction.
docstr_tok: ["Abstract","function","that","is","responsible","for","graph","construction",".","This","method","is","called","as","part","of","init",".","Subclasses","are","also","responsible","for","populating","the","variables","in","the","__init__","method","as","part","of","graph","construction","."]
code: def _construct_graph(self):
      """Abstract function that is responsible for graph construction.
  
      This method is called as part of init. Subclasses are also responsible for
      populating the variables in the __init__ method as part of graph
      construction.
      """
      raise NotImplementedError
  
imports: import abc
  import itertools
  import threading
  from typing import Any, Dict, List, NamedTuple, Tuple
  import apache_beam
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import constants
  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 EvalMetricsGraph obj's underlying member method code with example usage: obj._construct_graph() without return types