code: def _check_binary_multilabel_cases(
      self, output: Sequence[torch.Tensor]
  ) -> None:
      y_pred, y = output
  
      if not torch.equal(y, y**2):
          raise ValueError(
              "For binary cases, y must be comprised of 0's and 1's."
          )
  
      if not torch.equal(y_pred, y_pred**2):
          raise ValueError(
              "For binary cases, y_pred must be comprised of 0's and 1's."
          )
  
file_path: ignite/ignite/metrics/accuracy.py
imports: from typing import Callable, Optional, Sequence, Tuple, Union
  import torch
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 4 for calling the _BaseClassification obj's underlying member method code with example usage: obj._check_binary_multilabel_cases(output) without return types