code: def test_integration(metric_cls, true_result_fn, shape):
      assert len(shape) > 0 and len(shape) < 3
  
      custom_variable = 10.0 + 5.0 * torch.rand(shape)
  
      def update_fn(engine, batch):
          output = custom_variable[engine.state.iteration - 1]
          output = output.item() if output.ndimension() < 1 else output
          return 0, output
  
      engine = Engine(update_fn)
  
      custom_var_mean = metric_cls(
          output_transform=lambda output: output[1]
      )
      custom_var_mean.attach(engine, "agg_custom_var")
  
      state = engine.run([0] * shape[0])
  
      np.testing.assert_almost_equal(
          np.array(state.metrics["agg_custom_var"]),
          true_result_fn(custom_variable),
          decimal=5,
      )
  
      metric_state = custom_var_mean.state_dict()
      saved_num_examples = custom_var_mean.num_examples
      saved_accumulator = custom_var_mean.accumulator
      custom_var_mean.reset()
      assert custom_var_mean.num_examples == 0
      assert custom_var_mean.accumulator == 0
      custom_var_mean.load_state_dict(metric_state)
      assert custom_var_mean.num_examples == saved_num_examples
      assert (custom_var_mean.accumulator == saved_accumulator).all()
  
imports: import os
  import numpy
  import pytest
  import torch
  from torch.nn import Linear
  from torch.optim import SGD
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics.accumulation import Average, GeometricAverage, VariableAccumulation
label_desc: Use image node_id 7 for calling a global function with example usage: test_integration(metric_cls, true_result_fn, shape) and returns: int,  output