code: def test_linear_model() -> None:
      model1 = FeedForward(2, 10)
      model2 = FeedForward(2, 10)
  
      vanilla_model = train_linear_model(model1, False)
      scaled_model = train_linear_model(model2, True)
  
      for elt in zip(
          get_params_with_grad(vanilla_model),
          get_params_with_grad(scaled_model),
      ):
          assert torch.allclose(elt[0], elt[1])
  
imports: import logging
  import os
  from typing import Any, List, Tuple, Union
  import numpy
  import pytest
  from sklearn.datasets import make_blobs
  import torch
  from torch.cuda.amp.autocast_mode import autocast
  import torch.nn
  from torch.utils.data import DataLoader
  import torchvision
  import torchvision.transforms
  from fairscale.fair_dev.common_paths import DATASET_CACHE_ROOT
  from fairscale.fair_dev.testing.testing import skip_a_test_if_in_CI
  from fairscale.optim.layerwise_gradient_scaler import LayerwiseGradientScaler
label_desc: Use image node_id 5 for calling a global function with example usage: test_linear_model() without return types