code: def test_integration(attach_pr_re):
      torch.manual_seed(1)
  
      n_iters = 10
      batch_size = 10
      n_classes = 10
  
      y_true = torch.arange(0, n_iters * batch_size) % n_classes
      y_pred = 0.2 * torch.rand(n_iters * batch_size, n_classes)
      for i in range(n_iters * batch_size):
          if torch.rand(1) > 0.4:
              y_pred[i, y_true[i]] = 1.0
          else:
              j = torch.randint(0, n_classes, size=(1,))
              y_pred[i, j] = 0.7
  
      y_true_batch_values = iter(y_true.reshape(n_iters, batch_size))
      y_pred_batch_values = iter(
          y_pred.reshape(n_iters, batch_size, n_classes)
      )
  
      def update_fn(engine, batch):
          y_true_batch = next(y_true_batch_values)
          y_pred_batch = next(y_pred_batch_values)
          return y_pred_batch, y_true_batch
  
      evaluator = Engine(update_fn)
  
      precision = Precision(average=False)
      recall = Recall(average=False)
  
      def Fbeta(r, p, beta):
          return torch.mean(
              (1 + beta**2) * p * r / (beta**2 * p + r)
          ).item()
  
      F1 = MetricsLambda(Fbeta, recall, precision, 1)
  
      if attach_pr_re:
          precision.attach(evaluator, "precision")
          recall.attach(evaluator, "recall")
      F1.attach(evaluator, "f1")
  
      data = list(range(n_iters))
      state = evaluator.run(data, max_epochs=1)
  
      precision_true = precision_score(
          y_true, y_pred.argmax(dim=-1), average=None
      )
      recall_true = recall_score(
          y_true, y_pred.argmax(dim=-1), average=None
      )
      f1_true = f1_score(y_true, y_pred.argmax(dim=-1), average="macro")
  
      assert f1_true == approx(
          state.metrics["f1"]
      ), f"{f1_true} vs {state.metrics['f1']}"
      if attach_pr_re:
          precision = state.metrics["precision"].numpy()
          recall = state.metrics["recall"].numpy()
  
          assert precision_true == approx(
              precision
          ), f"{precision_true} vs {precision}"
          assert recall_true == approx(
              recall
          ), f"{recall_true} vs {recall}"
  
      metric_state = F1.state_dict()
      F1.reset()
      F1.load_state_dict(metric_state)
      f1_value = F1.compute()
      assert f1_value == state.metrics["f1"]
  
imports: import os
  import numpy
  import pytest
  import torch
  from pytest import approx
  from sklearn.metrics import f1_score, precision_score, recall_score
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.metrics import Accuracy, Metric, MetricsLambda, Precision, Recall
label_desc: Use image node_id 5 for calling a global function with example usage: test_integration(attach_pr_re) and returns: y_pred_batch,  y_true_batch, torch