code: def test_sequence_accuracy_metric():
      target = torch.tensor(
          [
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
          ]
      )
      preds = torch.tensor(
          [
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 6, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
              [1, 4, 5, 4, 0],
          ]
      )
      metric = metric_modules.SequenceAccuracyMetric()
      with metric.sync_context():
          metric.update(preds, target)
          assert torch.isclose(
              metric.compute(), torch.tensor(0.8438), rtol=0.0001
          )
  
imports: import pytest
  import torch
  from ludwig.distributed import init_dist_strategy
  from ludwig.modules import metric_modules
  from ludwig.schema.features.loss.loss import BWCEWLossConfig, SigmoidCrossEntropyLossConfig, SoftmaxCrossEntropyLossConfig
label_desc: Use image node_id 11 for calling a global function with example usage: test_sequence_accuracy_metric() without return types