code: def test_double_use_of_prefix_with_metriccollection():
      """Test that the expected output is produced when using prefix/postfix with metric collection.
  
      See issue: https://github.com/Lightning-AI/torchmetrics/issues/1915
  
      """
      category_names = ["Tree", "Bush"]
      num_classes = len(category_names)
  
      input_ = torch.rand((5, 2, 3, 3))
      target = torch.ones((5, 2, 3, 3)).long()
  
      val_metrics = MetricCollection(
          {
              "accuracy": MulticlassAccuracy(num_classes=num_classes),
              "f1": ClasswiseWrapper(
                  MulticlassF1Score(
                      num_classes=num_classes, average="none"
                  ),
                  category_names,
                  prefix="f_score_",
              ),
          },
          prefix="val/",
      )
  
      res = val_metrics(input_, target)
      assert "val/accuracy" in res
      assert "val/f_score_Tree" in res
      assert "val/f_score_Bush" in res
  
file_path: torchmetrics/tests/unittests/wrappers/test_classwise.py
imports: import pytest
  import torch
  from torchmetrics import MetricCollection
  from torchmetrics.classification import MulticlassAccuracy, MulticlassF1Score, MulticlassRecall
  from torchmetrics.wrappers import ClasswiseWrapper
label_desc: Use image node_id 7 for calling a global function with example usage: test_double_use_of_prefix_with_metriccollection() without return types