code: def test_load_state_dict():
      acc = Accuracy()
      error = 1.0 - acc
  
      acc.update(
          (
              torch.randint(0, 2, size=(8,)),
              torch.randint(0, 2, size=(8,)),
          )
      )
  
      e = error.compute()
      a = acc.compute()
      assert 1.0 - a == e
  
      metric_state = error.state_dict()
      error.reset()
      error.load_state_dict(metric_state)
      e2 = error.compute()
      assert e2 == e
  
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 6 for calling a global function with example usage: test_load_state_dict() without return types