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.
class_docstr_tok: ["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","."]
code: def _reset_metric_variables(self) -> None:
      # Lock should be acquired before calling this function.
      self._session.run(self._reset_variables_op)
  
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 17 for calling the EvalMetricsGraph obj's underlying member method code with example usage: obj._reset_metric_variables() without return types