code: def _test_linear_on_device(
      self, model, config, expected_shape, device, also_prune_bias
  ):
      model = model.to(device)
      model.eval()
      num_original_params = sum(p.numel() for p in model.parameters())
      x = torch.ones(128, 7, device=device)
  
      pruner = ImplementedPruner({"prune_bias": also_prune_bias})
      pruner.prepare(model, config)
      pruner.enable_mask_update = True
      pruner.step()
  
      y_expected = model(x)
  
      assert y_expected.shape == (128, 10)
      self._check_pruner_prepared(model, pruner, device)
  
      # Pruning step
      pruned = pruner.prune()
      y_pruned = pruned(x)
      num_pruned_params = sum(p.numel() for p in pruned.parameters())
  
      assert y_pruned.shape == expected_shape
      self._check_pruner_pruned(model, pruner, device)
      if y_pruned.shape == y_expected.shape:
          assert torch.isclose(
              y_expected, y_pruned, rtol=1e-05, atol=1e-07
          ).all()
          assert num_pruned_params < num_original_params
  
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 15 for calling the TestBaseStructuredSparsifier obj's underlying member method code with example usage: obj._test_linear_on_device(model, config, expected_shape, device, also_prune_bias) without return types