code: def test_covariance_unmasked_computation(self, device: str):
      covariance = Covariance()
      batch_size = 100
      num_labels = 10
      predictions = torch.randn(batch_size, num_labels, device=device)
      labels = 0.5 * predictions + torch.randn(
          batch_size, num_labels, device=device
      )
  
      stride = 10
  
      for i in range(batch_size // stride):
          timestep_predictions = predictions[
              stride * i : stride * (i + 1), :
          ]
          timestep_labels = labels[stride * i : stride * (i + 1), :]
          # Flatten the predictions and labels thus far, so numpy treats them as
          # independent observations.
          expected_covariance = np.cov(
              predictions[: stride * (i + 1), :].view(-1).cpu().numpy(),
              labels[: stride * (i + 1), :].view(-1).cpu().numpy(),
          )[0, 1]
          covariance(timestep_predictions, timestep_labels)
          assert_allclose(expected_covariance, covariance.get_metric())
  
      # Test reset
      covariance.reset()
      covariance(predictions, labels)
      assert_allclose(
          np.cov(
              predictions.view(-1).cpu().numpy(),
              labels.view(-1).cpu().numpy(),
          )[0, 1],
          covariance.get_metric(),
      )
  
file_path: allennlp/tests/training/metrics/covariance_test.py
imports: import numpy
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.testing import AllenNlpTestCase, multi_device
  from allennlp.training.metrics import Covariance
label_desc: Use image node_id 1 for calling the CovarianceTest obj's underlying member method code with example usage: obj.test_covariance_unmasked_computation(device) without return types