text: from typing import Callable, Sequence, Union
  
  import torch
  
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
  
  __all__ = ["TopKCategoricalAccuracy"]
  
  
  class TopKCategoricalAccuracy(Metric):
      """
      Calculates the top-k categorical accuracy.
  
      - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
  
      Args:
          k: the k in “top-k”.
          output_transform: a callable that is used to transform the
              :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
              form expected by the metric. This can be useful if, for example, you have a multi-output model and
              you want to compute the metric with respect to one of the outputs.
              By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
          device: specifies which device updates are accumulated on. Setting the
              metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
              non-blocking. By default, CPU.
  
      Examples:
          To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
          The output of the engine's ``process_function`` needs to be in the format of
          ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
          to the metric to transform the output into the form expected by the metric.
  
          .. testcode::
  
              def process_function(engine, batch):
                  y_pred, y = batch
                  return y_pred, y
  
              def one_hot_to_binary_output_transform(output):
                  y_pred, y = output
                  y = torch.argmax(y, dim=1)  # one-hot vector to label index vector
                  return y_pred, y
  
              engine = Engine(process_function)
              metric = TopKCategoricalAccuracy(
                  k=2, output_transform=one_hot_to_binary_output_transform)
              metric.attach(engine, 'top_k_accuracy')
  
              preds = torch.Tensor([
                  [0.7, 0.2, 0.05, 0.05],     # 1 is in the top 2
                  [0.2, 0.3, 0.4, 0.1],       # 0 is not in the top 2
                  [0.4, 0.4, 0.1, 0.1],       # 0 is in the top 2
                  [0.7, 0.05, 0.2, 0.05]      # 2 is in the top 2
              ])
              target = torch.Tensor([         # targets as one-hot vectors
                  [0, 1, 0, 0],
                  [1, 0, 0, 0],
                  [1, 0, 0, 0],
                  [0, 0, 1, 0]
              ])
  
              state = engine.run([[preds, target]])
              print(state.metrics['top_k_accuracy'])
  
          .. testoutput::
  
              0.75
      """
  
      def __init__(
          self,
          k: int = 5,
          output_transform: Callable = lambda x: x,
          device: Union[str, torch.device] = torch.device("cpu"),
      ) -> None:
          super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device)
          self._k = k
  
      @reinit__is_reduced
      def reset(self) -> None:
          self._num_correct = torch.tensor(0, device=self._device)
          self._num_examples = 0
  
      @reinit__is_reduced
      def update(self, output: Sequence[torch.Tensor]) -> None:
          y_pred, y = output[0].detach(), output[1].detach()
          sorted_indices = torch.topk(y_pred, self._k, dim=1)[1]
          expanded_y = y.view(-1, 1).expand(-1, self._k)
          correct = torch.sum(torch.eq(sorted_indices, expanded_y), dim=1)
  
          self._num_correct += torch.sum(correct).to(self._device)
          self._num_examples += correct.shape[0]
  
      @sync_all_reduce("_num_correct", "_num_examples")
      def compute(self) -> Union[float, torch.Tensor]:
          if self._num_examples == 0:
              raise NotComputableError(
                  "TopKCategoricalAccuracy must have at least one example before it can be computed."
              )
          return self._num_correct.item() / self._num_examples
  