code: def _test_prepare_linear_on_device(self, model, device):
      model = copy.deepcopy(model).to(device)
      x = torch.ones(128, 7, device=device)
      pruner = SimplePruner(None)
      pruner.prepare(model, None)
      self._check_pruner_prepared(model, pruner, device)
      assert model(x).shape == (128, 10)
  
imports: import copy
  import logging
  import random
  import torch
  from torch import nn
  from torch.ao.pruning._experimental.pruner import SaliencyPruner, LSTMSaliencyPruner, BaseStructuredSparsifier, FakeStructuredSparsity, FPGMPruner
  from torch.nn.utils import parametrize
  from torch.testing._internal.common_utils import TestCase, skipIfTorchDynamo
  from torch.testing._internal.common_pruning import SimpleLinear, LinearBias, LinearActivation, LinearActivationFunctional, SimpleConv2d, Conv2dBias, Conv2dActivation, Conv2dPadBias, Conv2dPool, Conv2dPoolFlatten, Conv2dPoolFlattenFunctional, LSTMLinearModel, LSTMLayerNormLinearModel, rows_are_subset
label_desc: Use image node_id 6 for calling the TestBaseStructuredSparsifier obj's underlying member method code with example usage: obj._test_prepare_linear_on_device(model, device) without return types