class_docstr: Test class that allows passing base metric as a class rather than its instantiation to the wrapper.
class_docstr_tok: ["Test","class","that","allows","passing","base","metric","as","a","class","rather","than","its","instantiation","to","the","wrapper","."]
docstr: Run forward on the underlying metric.
docstr_tok: ["Run","forward","on","the","underlying","metric","."]
code: def forward(self, *args: Any, **kwargs: Any):
      """Run forward on the underlying metric."""
      return self.metric(*args, **kwargs)
  
imports: from functools import partial
  from typing import Any
  import pytest
  import torch
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import r2_score
  from torch import Tensor, tensor
  from torchmetrics import Metric
  from torchmetrics.classification import ConfusionMatrix, MulticlassAccuracy
  from torchmetrics.regression import R2Score
  from torchmetrics.wrappers.multioutput import MultioutputWrapper
  from unittests import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES, _Input
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 4 for calling the _MultioutputMetric obj's underlying member method code with example usage: obj.forward() and returns: self