code: def test_metrics_lambda_update_and_attach_together():
      y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
      y = torch.randint(0, 2, size=(15, 10, 4)).long()
  
      def update_fn(engine, batch):
          y_pred, y = batch
          return y_pred, y
  
      engine = 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)
  
      F1.attach(engine, "f1")
      with pytest.raises(
          ValueError,
          match=r"MetricsLambda is already attached to an engine",
      ):
          F1.update((y_pred, y))
  
      y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
      y = torch.randint(0, 2, size=(15, 10, 4)).long()
  
      F1 = MetricsLambda(Fbeta, recall, precision, 1)
      F1.update((y_pred, y))
  
      engine = Engine(update_fn)
  
      with pytest.raises(
          ValueError,
          match=r"The underlying metrics are already updated",
      ):
          F1.attach(engine, "f1")
  
      F1.reset()
      F1.attach(engine, "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 3 for calling a global function with example usage: test_metrics_lambda_update_and_attach_together() and returns: y_pred,  y, torch