code: def test_accuracy():
      acc_calculator = Accuracy(average=False, topk=(2,))
  
      # start with multiclass classification
      pred = torch.FloatTensor([[0.4, 0.5, 0.1], [0.1, 0.8, 0.1]])
      target = torch.LongTensor([[2], [1]])
  
      assert pred.size() == torch.Size([2, 3])
  
      # check for length of the data
      for i in range(2):
          acc_calculator.update(
              output=pred[i, :].unsqueeze(0), target=target[i, :]
          )
  
      assert acc_calculator.value.shape == (2, 1)
      assert np.allclose(acc_calculator.value, np.array([[0], [1]]))
  
      acc_calculator = Accuracy(average=True, topk=(2,))
  
      # check for length of the data
      for i in range(2):
          acc_calculator.update(
              output=pred[i, :].unsqueeze(0), target=target[i, :]
          )
  
      assert acc_calculator.value == 0.5
  
imports: import os
  from pathlib import Path
  import numpy
  import pytest
  import shutil
  import torch
  from hypothesis import given
  from torch_hypothesis import classification_logits_and_labels
  from baal.utils.metrics import Loss, Accuracy, Precision, ECE, ClassificationReport, PRAuC, ECE_PerCLs
label_desc: Use image node_id 9 for calling a global function with example usage: test_accuracy() without return types