class_docstr: The base class for all structured metrics used within TFMA.
  
  This class allows custom metrics to control how proto serialization happens,
  and how to handle basic algebraic operations used in computing confidence
  intervals and model diffs. By implementing the _apply_binary_op methods,
  subclasses can then be treated like primitive numeric types.
class_docstr_tok: ["The","base","class","for","all","structured","metrics","used","within","TFMA",".","This","class","allows","custom","metrics","to","control","how","proto","serialization","happens",",","and","how","to","handle","basic","algebraic","operations","used","in","computing","confidence","intervals","and","model","diffs",".","By","implementing","the","_apply_binary_op","methods",",","subclasses","can","then","be","treated","like","primitive","numeric","types","."]
code: def __truediv__(
      self: ConcreteStructuredMetricValue,
      other: Union[ConcreteStructuredMetricValue, float],
  ):
      return self._apply_binary_op(other, operator.truediv)
  
imports: import abc
  import datetime
  import operator
  from typing import Any, Callable, Dict, Iterable, List, MutableMapping, NamedTuple, Optional, TypeVar, Tuple, Union
  from apache_beam.utils import shared
  import numpy
  import six
  import tensorflow
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
label_desc: Use image node_id 8 for calling the StructuredMetricValue obj's underlying member method code with example usage: obj.__truediv__(other) and returns: self