code: def test_none_reduction():
      pred = torch.rand(20, 10)
      target = torch.rand(20, 10)
  
      mae = MAE(reduction="none")(pred, target)
      assert (
          mae.size() == pred.size()
      ), "dimension should not change if reduction is none"
  
file_path: pytorch-forecasting/tests/test_metrics.py
imports: import itertools
  import numpy
  import pytest
  import torch
  from torch.nn.utils import rnn
  from pytorch_forecasting.data.encoders import TorchNormalizer
  from pytorch_forecasting.metrics import MAE, SMAPE, BetaDistributionLoss, ImplicitQuantileNetworkDistributionLoss, LogNormalDistributionLoss, MultivariateNormalDistributionLoss, NegativeBinomialDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.metrics.base_metrics import AggregationMetric, CompositeMetric
label_desc: Use image node_id 3 for calling a global function with example usage: test_none_reduction() without return types