code: def test_binary_wrong_inputs():
      acc = Accuracy()
  
      with pytest.raises(
          ValueError,
          match=r"For binary cases, y must be comprised of 0's and 1's",
      ):
          # y has not only 0 or 1 values
          acc.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.arange(0, 10).long(),
              )
          )
  
      with pytest.raises(
          ValueError,
          match=r"For binary cases, y_pred must be comprised of 0's and 1's",
      ):
          # y_pred values are not thresholded to 0, 1 values
          acc.update(
              (torch.rand(10), torch.randint(0, 2, size=(10,)).long())
          )
  
      with pytest.raises(ValueError, match=r"y must have shape of "):
          # incompatible shapes
          acc.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.randint(0, 2, size=(10, 5)).long(),
              )
          )
  
      with pytest.raises(ValueError, match=r"y must have shape of "):
          # incompatible shapes
          acc.update(
              (
                  torch.randint(0, 2, size=(10, 5, 6)).long(),
                  torch.randint(0, 2, size=(10,)).long(),
              )
          )
  
      with pytest.raises(ValueError, match=r"y must have shape of "):
          # incompatible shapes
          acc.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.randint(0, 2, size=(10, 5, 6)).long(),
              )
          )
  
imports: import os
  import pytest
  import torch
  from packaging.version import Version
  from sklearn.metrics import accuracy_score
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Accuracy
label_desc: Use image node_id 4 for calling a global function with example usage: test_binary_wrong_inputs() without return types