code: def test_integration():
      def _test(y_pred, y, batch_size):
          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 = FractionalAbsoluteError()
          m.attach(engine, "fab")
  
          np_y = y.numpy().ravel()
          np_y_pred = y_pred.numpy().ravel()
  
          data = list(range(y_pred.shape[0] // batch_size))
          fab = engine.run(data, max_epochs=1).metrics["fab"]
  
          np_sum = (
              2
              * np.abs((np_y_pred - np_y))
              / (np.abs(np_y_pred) + np.abs(np_y))
          ).sum()
          np_len = len(y_pred)
          np_ans = np_sum / np_len
  
          assert np_ans == pytest.approx(fab)
  
      def get_test_cases():
          test_cases = [
              (torch.rand(size=(100,)), torch.rand(size=(100,)), 10),
              (
                  torch.rand(size=(100, 1)),
                  torch.rand(size=(100, 1)),
                  20,
              ),
          ]
          return test_cases
  
      for _ in range(5):
          # check multiple random inputs as random exact occurencies are rare
          test_cases = get_test_cases()
          for y_pred, y, batch_size in test_cases:
              _test(y_pred, y, batch_size)
  
imports: import os
  import numpy
  import pytest
  import torch
  import ignite.distributed
  from ignite.contrib.metrics.regression import FractionalAbsoluteError
  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: test_cases, torch,  torch