code: def test_reset(self, device: str):
      spearman_correlation = SpearmanCorrelation()
      batch_size = 10
      num_labels = 10
      predictions = torch.randn(batch_size, num_labels, device=device)
      labels = 0.5 * predictions + torch.randn(
          batch_size, num_labels, device=device
      )
  
      # 1.test spearman_correlation.reset()
      spearman_correlation.reset()
      spearman_correlation(predictions, labels)
      temp = spearman_correlation.get_metric()
      spearman_correlation.reset()
      spearman_correlation(predictions, labels)
      assert spearman_correlation.get_metric() == temp
  
      # 2.test spearman_correlation.reset()
      spearman_correlation.reset()
      spearman_correlation(predictions, labels)
  
      spearman_correlation.get_metric(reset=False)
      assert spearman_correlation.get_metric() != float("NaN")
      spearman_correlation.get_metric(reset=True)
      assert math.isnan(spearman_correlation.get_metric())
  
file_path: allennlp/tests/training/metrics/spearman_correlation_test.py
imports: import math
  import pytest
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import SpearmanCorrelation
label_desc: Use image node_id 3 for calling the SpearmanCorrelationTest obj's underlying member method code with example usage: obj.test_reset(device) without return types