title: Precision / Recall for multi-label classification using torchmetrics
body: <p>Is there a function or a set of arguments that I can use in order to calculate Precision and Recall for a multi-label problem?</p>
  <p>Note that with multi-label I mean that each sample can be classified into more than one class.</p>
  <p>The following is not returning what I would expect:</p>
  <pre><code>import torch
  from torchmetrics import Precision
  target = torch.tensor([
      [0, 0, 1, 1, 0],  # Sample 1 belongs to class 2 and 3 (zero-indexed)
      [0, 0, 1, 0, 0], # Sample 2 belongs to class 2 (zero-indexed)
  ])
  preds = torch.tensor([
      [0, 0, 0, 0, 0],  # Sample 1 predicted to belong to no class
      [0, 0, 0, 0, 0], # Sample 2 predicted to belong to no class
  ])
  metric = Precision(num_classes=5, mdmc_average=&quot;samplewise&quot;)
  print(metric(preds, target))
  </code></pre>
  <p>It returns: <code>tensor(0.7000)</code>, but it should be 0% since there are no True Positives.</p>