code: def test_torchmetric(self):
      mse_fn = lambda: torchmetrics.MeanSquaredError()
      corr_fn = lambda: torchmetrics.SpearmanCorrCoef()
      wrapper = ModelWrapper(self.model, self.criterion)
      wrapper.add_metric("mse", mse_fn)
      wrapper.add_metric("corr", corr_fn)
      wrapper.train_on_dataset(
          self.dataset,
          self.optim,
          batch_size=32,
          epoch=1,
          use_cuda=False,
      )
      wrapper.test_on_dataset(
          self.dataset, batch_size=32, use_cuda=False
      )
      metrics = wrapper.get_metrics()
      assert {
          "train_corr",
          "test_corr",
          "train_mse",
          "test_mse",
      }.issubset(
          metrics.keys()
      )  # Torchmetric metric
      assert {"train_loss", "test_loss"}.issubset(metrics.keys())
  
imports: import math
  import unittest
  from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  import torchmetrics
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from baal.modelwrapper import ModelWrapper, mc_inference
  from baal.utils.metrics import ClassificationReport
label_desc: Use image node_id 11 for calling the ModelWrapperTest obj's underlying member method code with example usage: obj.test_torchmetric() without return types