text: from copy import deepcopy
  from typing import Any, Callable, List, Optional, Tuple
  
  import torch
  from torch import nn
  
  from torchmetrics import Metric
  from torchmetrics.utilities import apply_to_collection
  
  
  def _get_nan_indices(*tensors: torch.Tensor) -> torch.Tensor:
      """Get indices of rows along dim 0 which have NaN values."""
      if len(tensors) == 0:
          raise ValueError("Must pass at least one tensor as argument")
      sentinel = tensors[0]
      nan_idxs = torch.zeros(len(sentinel), dtype=torch.bool, device=sentinel.device)
      for tensor in tensors:
          permuted_tensor = tensor.flatten(start_dim=1)
          nan_idxs |= torch.any(torch.isnan(permuted_tensor), dim=1)
      return nan_idxs
  
  
  class MultioutputWrapper(Metric):
      """Wrap a base metric to enable it to support multiple outputs.
  
      Several torchmetrics metrics, such as :class:`torchmetrics.regression.spearman.SpearmanCorrcoef` lack support for
      multioutput mode. This class wraps such metrics to support computing one metric per output.
      Unlike specific torchmetric metrics, it doesn't support any aggregation across outputs.
      This means if you set `num_outputs` to 2, `compute()` will return a Tensor of dimension
      (2, ...) where ... represents the dimensions the metric returns when not wrapped.
  
      In addition to enabling multioutput support for metrics that lack it, this class also supports, albeit in a crude
      fashion, dealing with missing labels (or other data). When ``remove_nans`` is passed, the class will remove the
      intersection of NaN containing "rows" upon each update for each output. For example, suppose a user uses
      `MultioutputWrapper` to wrap :class:`torchmetrics.regression.r2.R2Score` with 2 outputs, one of which occasionally
      has missing labels for classes like ``R2Score`` is that this class supports removing NaN values
      (parameter ``remove_nans``) on a per-output basis. When ``remove_nans`` is passed the wrapper will remove all rows
  
      Args:
          base_metric:
              Metric being wrapped.
          num_outputs:
              Expected dimensionality of the output dimension. This parameter is
              used to determine the number of distinct metrics we need to track.
          output_dim:
              Dimension on which output is expected. Note that while this provides some flexibility, the output dimension
              must be the same for all inputs to update. This applies even for metrics such as `Accuracy` where the labels
              can have a different number of dimensions than the predictions. This can be worked around if the output
              dimension can be set to -1 for both, even if -1 corresponds to different dimensions in different inputs.
          remove_nans:
              Whether to remove the intersection of rows containing NaNs from the values passed through to each underlying
              metric. Proper operation requires all tensors passed to update to have dimension `(N, ...)` where N
              represents the length of the batch or dataset being passed in.
          squeeze_outputs:
              If true, will squeeze the 1-item dimensions left after `index_select` is applied.
              This is sometimes unnecessary but harmless for metrics such as `R2Score` but useful
              for certain classification metrics that can't handle additional 1-item dimensions.
          compute_on_step:
              Forward only calls ``update()`` and returns None if this is set to False.
  
              .. deprecated:: v0.8
                  Argument has no use anymore and will be removed v0.9.
  
          dist_sync_on_step:
              Required for distributed training support.
          process_group:
              Specify the process group on which synchronization is called.
              The
          dist_sync_fn:
              Required for distributed training support.
  
      Example:
  
           >>> # Mimic R2Score in `multioutput`, `raw_values` mode:
           >>> import torch
           >>> from torchmetrics import MultioutputWrapper, R2Score
           >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
           >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
           >>> r2score = MultioutputWrapper(R2Score(), 2)
           >>> r2score(preds, target)
           [tensor(0.9654), tensor(0.9082)]
           >>> # Classification metric where prediction and label tensors have different shapes.
           >>> from torchmetrics import BinnedAveragePrecision
           >>> target = torch.tensor([[1, 2], [2, 0], [1, 2]])
           >>> preds = torch.tensor([
           ...     [[.1, .8], [.8, .05], [.1, .15]],
           ...     [[.1, .1], [.2, .3], [.7, .6]],
           ...     [[.002, .4], [.95, .45], [.048, .15]]
           ... ])
           >>> binned_avg_precision = MultioutputWrapper(BinnedAveragePrecision(3, thresholds=5), 2)
           >>> binned_avg_precision(preds, target)
           [[tensor(-0.), tensor(1.0000), tensor(1.0000)], [tensor(0.3333), tensor(-0.), tensor(0.6667)]]
      """
  
      is_differentiable = False
  
      def __init__(
          self,
          base_metric: Metric,
          num_outputs: int,
          output_dim: int = -1,
          remove_nans: bool = True,
          squeeze_outputs: bool = True,
          compute_on_step: Optional[bool] = None,
          dist_sync_on_step: bool = False,
          process_group: Optional[Any] = None,
          dist_sync_fn: Callable = None,
      ):
          super().__init__(
              compute_on_step=compute_on_step,
              dist_sync_on_step=dist_sync_on_step,
              process_group=process_group,
              dist_sync_fn=dist_sync_fn,
          )
          self.metrics = nn.ModuleList([deepcopy(base_metric) for _ in range(num_outputs)])
          self.output_dim = output_dim
          self.remove_nans = remove_nans
          self.squeeze_outputs = squeeze_outputs
  
      def _get_args_kwargs_by_output(
          self, *args: torch.Tensor, **kwargs: torch.Tensor
      ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
          """Get args and kwargs reshaped to be output-specific and (maybe) having NaNs stripped out."""
          args_kwargs_by_output = []
          for i in range(len(self.metrics)):
              selected_args = apply_to_collection(
                  args, torch.Tensor, torch.index_select, dim=self.output_dim, index=torch.tensor(i, device=self.device)
              )
              selected_kwargs = apply_to_collection(
                  kwargs, torch.Tensor, torch.index_select, dim=self.output_dim, index=torch.tensor(i, device=self.device)
              )
              if self.remove_nans:
                  args_kwargs = selected_args + tuple(selected_kwargs.values())
                  nan_idxs = _get_nan_indices(*args_kwargs)
                  selected_args = [arg[~nan_idxs] for arg in selected_args]
                  selected_kwargs = {k: v[~nan_idxs] for k, v in selected_kwargs.items()}
  
              if self.squeeze_outputs:
                  selected_args = [arg.squeeze(self.output_dim) for arg in selected_args]
              args_kwargs_by_output.append((selected_args, selected_kwargs))
          return args_kwargs_by_output
  
      def update(self, *args: Any, **kwargs: Any) -> None:
          """Update each underlying metric with the corresponding output."""
          reshaped_args_kwargs = self._get_args_kwargs_by_output(*args, **kwargs)
          for metric, (selected_args, selected_kwargs) in zip(self.metrics, reshaped_args_kwargs):
              metric.update(*selected_args, **selected_kwargs)
  
      def compute(self) -> List[torch.Tensor]:
          """Compute metrics."""
          return [m.compute() for m in self.metrics]
  
      @torch.jit.unused
      def forward(self, *args: Any, **kwargs: Any) -> Any:
          """Call underlying forward methods and aggregate the results if they're non-null.
  
          We override this method to ensure that state variables get copied over on the underlying metrics.
          """
          results = []
          reshaped_args_kwargs = self._get_args_kwargs_by_output(*args, **kwargs)
          for metric, (selected_args, selected_kwargs) in zip(self.metrics, reshaped_args_kwargs):
              results.append(metric(*selected_args, **selected_kwargs))
          if results[0] is None:
              return None
          return results
  
      def reset(self) -> None:
          """Reset all underlying metrics."""
          for metric in self.metrics:
              metric.reset()
  