code: def _compute(self):
      device = (
          f"cuda:{dist.get_rank()}"
          if torch.cuda.is_available()
          else "cpu"
      )
      metric = MeanAbsoluteError()
  
      def _val_func(engine, batch):
          pass
  
      engine = Engine(_val_func)
      metric.attach(engine, "MAE")
  
      # get testing data
      batch = BATCH_SIZE
      base = BASE_DIM_SIZE
      spatial = SPATIAL_DIM
      in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1))
      in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1))
  
      in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1))
      in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1))
  
      if dist.get_rank() == 0:
          y_pred = in_tensor_a1.to(device)
          y = in_tensor_b1.to(device)
          metric.update([y_pred, y])
  
      if dist.get_rank() == 1:
          y_pred = in_tensor_a2.to(device)
          y = in_tensor_b2.to(device)
          metric.update([y_pred, y])
  
      out_tensor = metric.compute()
  
      # do numpy functions to get ground truth referece
      out_tensor_np1 = maemetric_np(
          y_pred=in_tensor_a1.cpu().numpy(),
          y=in_tensor_b1.cpu().numpy(),
      )
      out_tensor_np2 = maemetric_np(
          y_pred=in_tensor_a2.cpu().numpy(),
          y=in_tensor_b2.cpu().numpy(),
      )
      out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0
  
      np.testing.assert_allclose(
          out_tensor, out_tensor_np, rtol=1e-04, atol=1e-04
      )
  
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  import torch.distributed
  from ignite.engine import Engine
  from monai.handlers import MeanAbsoluteError, MeanSquaredError, PeakSignalToNoiseRatio, RootMeanSquaredError
  from monai.utils import set_determinism
  from tests.utils import DistCall, DistTestCase
label_desc: Use image node_id 2 for calling the DistributedMeanAbsoluteError obj's underlying member method code with example usage: obj._compute() without return types