code: def _test_metric(
      self, metric, should_check_differentiable=True, **kwargs
  ):
      init_value, final_value = self.check_metric(metric, **kwargs)
      # here the trailing dimension is the number of tasks
      self.assertEqual(
          final_value.shape,
          torch.Size(
              (
                  *self.train_y.shape[:-2],
                  self.train_y.shape[-1],
              )
          ),
      )
      self.assertTrue(
          torch.all(final_value.mean() <= init_value.mean())
      )
      if should_check_differentiable:
          self.assertTrue(final_value.requires_grad)
  
imports: import math
  import unittest
  import torch
  import gpytorch
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from gpytorch.kernels import MultitaskKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
  from gpytorch.means import ConstantMean, MultitaskMean
  from gpytorch.metrics import mean_absolute_error, mean_squared_error, mean_standardized_log_loss, negative_log_predictive_density, quantile_coverage_error, standardized_mean_squared_error
  from gpytorch.models import ExactGP
label_desc: Use image node_id 3 for calling the TestMetricsMultiTask obj's underlying member method code with example usage: obj._test_metric(metric, should_check_differentiable) without return types