code: def test_not_training():
      class AssertTraining(nn.Module):
          def forward(self, x):
              assert self.training
              return x
  
      model = nn.Sequential(AssertTraining())
  
      model.eval()
      assert not model.training
  
      sample = torch.rand(1)
      balance_by_time(1, model, sample, device="cpu")
  
      assert not model.training
  
imports: import time
  import pytest
  import torch
  from torch import nn
  from torch.distributed.pipeline.sync._balance import balance_by_size, balance_by_time, blockpartition
  from torch.distributed.pipeline.sync._balance.profile import layerwise_sandbox
  from torch.testing._internal.common_utils import run_tests
label_desc: Use image node_id 12 for calling a global function with example usage: test_not_training() and returns: x