class_docstr: Confusion Matrix accumulator with kwargs used to compute it.
  
  Confusion matrix for classification and retrieval tasks.
  See https://en.wikipedia.org/wiki/Confusion_matrix for more info.
  
  Attributes:
    k: a sequence of topk, sequentially corresponds to the actual confusion
      matrix counts (tp, tn, fp, fn).
    tp: True positives count.
    tn: True negatives count.
    fp: False positives count.
    fn: False negative count.
    dtype: data type of the instance numpy array. None by default, dtype is
      deduced by numpy at construction.
class_docstr_tok: ["Confusion","Matrix","accumulator","with","kwargs","used","to","compute","it",".","Confusion","matrix","for","classification","and","retrieval","tasks",".","See","https",":","\/\/en.wikipedia.org\/wiki\/Confusion_matrix","for","more","info",".","Attributes",":","k",":","a","sequence","of","topk",",","sequentially","corresponds","to","the","actual","confusion","matrix","counts","(","tp",",","tn",",","fp",",","fn",")",".","tp",":","True","positives","count",".","tn",":","True","negatives","count",".","fp",":","False","positives","count",".","fn",":","False","negative","count",".","dtype",":","data","type","of","the","instance","numpy","array",".","None","by","default",",","dtype","is","deduced","by","numpy","at","construction","."]
docstr: Numerically equals.
docstr_tok: ["Numerically","equals","."]
code: def __eq__(self, other):
      """Numerically equals."""
      return np.allclose(self.k, other.k) and super().__eq__(other)
  
imports: from collections.abc import Iterable, Sequence
  import dataclasses
  import enum
  import itertools
  from typing import Any
  import numpy
  from numpy import typing
  from tensorflow_model_analysis.experimental.lazytf import api
label_desc: Use image node_id 2 for calling the _TopKConfusionMatrix obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown