code: def __init__(
      self,
      output_transform: Callable = lambda x: x,
      is_multilabel: bool = False,
      device: Union[str, torch.device] = torch.device("cpu"),
  ):
      self._is_multilabel = is_multilabel
      self._type: Optional[str] = None
      self._num_classes: Optional[int] = None
      super(_BaseClassification, self).__init__(
          output_transform=output_transform, device=device
      )
  
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 1 to create a new _BaseClassification object from inherited base classes: Metric with example: obj = _BaseClassification(output_transform, is_multilabel, device)