code: def test_integration():
      y_pred = torch.rand(size=(100,))
      y = torch.rand(size=(100,))
  
      batch_size = 10
  
      def update_fn(engine, batch):
          idx = (engine.state.iteration - 1) * batch_size
          y_true_batch = np_y[idx : idx + batch_size]
          y_pred_batch = np_y_pred[idx : idx + batch_size]
          return torch.from_numpy(y_pred_batch), torch.from_numpy(
              y_true_batch
          )
  
      engine = Engine(update_fn)
  
      m = GeometricMeanRelativeAbsoluteError()
      m.attach(engine, "gmrae")
  
      np_y = y.numpy().ravel()
      np_y_pred = y_pred.numpy().ravel()
  
      data = list(range(y_pred.shape[0] // batch_size))
      gmrae = engine.run(data, max_epochs=1).metrics["gmrae"]
  
      sum_errors = np.log(
          np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())
      ).sum()
      np_len = len(y_pred)
      np_ans = np.exp(sum_errors / np_len)
  
      assert np_ans == pytest.approx(gmrae)
  
imports: import os
  import numpy
  import pytest
  import torch
  import ignite.distributed
  from ignite.contrib.metrics.regression import GeometricMeanRelativeAbsoluteError
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
label_desc: Use image node_id 4 for calling a global function with example usage: test_integration() and returns: torch,  torch