text: Title: How to compute metrics for each class in multi class segmentation
  Body: I would compute the metrics individually for each class so I would like to have in output a (1xC) vector where C is the number of classes, I was trying like this but it throws me an error:
  ```
  output = torch.rand([10, 3, 256, 256])
  target = torch.rand([10, 1, 256, 256]).round().long()
  
  # first compute statistics for true positives, false positives, false negative and
  # true negative "pixels"
  tp, fp, fn, tn = smp.metrics.get_stats(output, target, mode='multi class', num_classes = 3)
  
  # then compute metrics with required reduction (see metric docs)
  iou_score = smp.metrics.iou_score(tp, fp, fn, tn, reduction="macro-imagewise")
  f1_score = smp.metrics.f1_score(tp, fp, fn, tn, reduction="macro-imagewise")
  false_negatives = smp.metrics.false_negative_rate(tp, fp, fn, tn, reduction=None)
  recall = smp.metrics.recall(tp, fp, fn, tn, reduction=None)
  ```
  
  The error:
  ```
  ValueError: For ``multiclass`` mode ``target`` should be one of the integer types, got torch.float32.
  ```