code: def test_recursive_attachment():
      def _test(composed_metric, metric_name, compute_true_value_fn):
          metrics = {
              metric_name: composed_metric,
          }
  
          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
  
          validator = Engine(update_fn)
  
          for name, metric in metrics.items():
              metric.attach(validator, name)
  
          def data(y_pred, y):
              for i in range(y_pred.shape[0]):
                  yield (y_pred[i], y[i])
  
          d = data(y_pred, y)
          state = validator.run(
              d, max_epochs=1, epoch_length=y_pred.shape[0]
          )
  
          assert set(state.metrics.keys()) == set([metric_name])
          np_y_pred = y_pred.numpy().ravel()
          np_y = y.numpy().ravel()
          assert state.metrics[metric_name] == approx(
              compute_true_value_fn(np_y_pred, np_y)
          )
  
      precision_1 = Precision()
      precision_2 = Precision()
      summed_precision = precision_1 + precision_2
  
      def compute_true_summed_precision(y_pred, y):
          p1 = precision_score(y, y_pred)
          p2 = precision_score(y, y_pred)
          return p1 + p2
  
      _test(
          summed_precision,
          "summed precision",
          compute_true_value_fn=compute_true_summed_precision,
      )
  
      precision_1 = Precision()
      precision_2 = Precision()
      mean_precision = (precision_1 + precision_2) / 2
  
      def compute_true_mean_precision(y_pred, y):
          p1 = precision_score(y, y_pred)
          p2 = precision_score(y, y_pred)
          return (p1 + p2) * 0.5
  
      _test(
          mean_precision,
          "mean precision",
          compute_true_value_fn=compute_true_mean_precision,
      )
  
      precision_1 = Precision()
      precision_2 = Precision()
      some_metric = (
          2.0
          + 0.2
          * (precision_1 * precision_2 + precision_1 - precision_2)
          ** 0.5
      )
  
      def compute_true_somemetric(y_pred, y):
          p1 = precision_score(y, y_pred)
          p2 = precision_score(y, y_pred)
          return 2.0 + 0.2 * (p1 * p2 + p1 - p2) ** 0.5
  
      _test(some_metric, "some metric", compute_true_somemetric)
  
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 9 for calling a global function with example usage: test_recursive_attachment() and returns: unknown, unknown, unknown, y_pred,  y