code: def test_error_on_different_shape(metric_class=R2Score):
      """Test that error is raised on different shapes of input."""
      metric = metric_class()
      with pytest.raises(
          RuntimeError,
          match="Predictions and targets are expected to have the same shape",
      ):
          metric(torch.randn(100), torch.randn(50))
  
imports: from functools import partial
  import pytest
  import torch
  from sklearn.metrics import r2_score
  from torchmetrics.functional import r2_score
  from torchmetrics.regression import R2Score
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 3 for calling a global function with example usage: test_error_on_different_shape(metric_class) without return types