class_docstr: This set of tests verify the computation logic of accuracy in several
  corner cases that we know the computation results. The goal is to
  provide some confidence of the correctness of the math formula.
class_docstr_tok: ["This","set","of","tests","verify","the","computation","logic","of","accuracy","in","several","corner","cases","that","we","know","the","computation","results",".","The","goal","is","to","provide","some","confidence","of","the","correctness","of","the","math","formula","."]
code: def test_calc_accuracy_balanced(self) -> None:
      self.predictions["DefaultTask"] = torch.Tensor(
          [[0.0001 * x for x in range(10000)] * 2]
      )
      self.labels["DefaultTask"] = torch.Tensor(
          [[0] * 10000 + [1] * 10000]
      )
      self.weights["DefaultTask"] = torch.ones([1, 20000])
  
      expected_accuracy = torch.tensor([0.5], dtype=torch.double)
      self.accuracy.update(**self.batches)
      actual_accuracy = self.accuracy.compute()[
          "accuracy-DefaultTask|window_accuracy"
      ]
      torch.allclose(expected_accuracy, actual_accuracy)
  
imports: import unittest
  from typing import Dict, Iterable, Type, Union
  import torch
  from torch import no_grad
  from torchrec.metrics.accuracy import AccuracyMetric, compute_accuracy
  from torchrec.metrics.metrics_config import DefaultTaskInfo
  from torchrec.metrics.rec_metric import RecComputeMode, RecMetric
  from torchrec.metrics.test_utils import metric_test_helper, rec_metric_value_test_launcher, RecTaskInfo, TestMetric
label_desc: Use image node_id 4 for calling the AccuracyMetricValueTest obj's underlying member method code with example usage: obj.test_calc_accuracy_balanced() without return types