class_docstr: Abstraction for using a EvalSavedModel.
  
  Note that this class overrides eval_metrics_graph.EvalMetricsGraph
  which contains most of the functionality for handling the metric ops.
  In the eval saved model path, a common graph is shared between generation
  FeaturesPredictionLabels through Predict and doing the metric ops.
  The specific methods of this class constructs the graph and handles
  the prediction ops.
docstr: Returns the operation based on name stored in a tensor.
docstr_tok: ["Returns","the","operation","based","on","name","stored","in","a","tensor","."]
code: def _get_op_from_tensor(self, op_name_tensor: types.TensorType):
      """Returns the operation based on name stored in a tensor."""
      if op_name_tensor is None:
          return None
      op_name = self._session.run(op_name_tensor).decode("utf-8")
      return self._graph.get_operation_by_name(op_name)
  
imports: from typing import Any, Dict, Generator, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
  from tensorflow_model_analysis.eval_saved_model import constants
  from tensorflow_model_analysis.eval_saved_model import encoding
  from tensorflow_model_analysis.eval_saved_model import graph_ref
  from tensorflow_model_analysis.eval_saved_model import util
  from tensorflow.core.protobuf import meta_graph_pb2
label_desc: Use image node_id 4 for calling the EvalSavedModel obj's underlying member method code with example usage: obj._get_op_from_tensor(op_name_tensor) and returns: self, None