code: def test_prune_conv2d_padding_conv2d(self):
      # Conv2d with Padded layers after Bias layers
      configs, shapes = [], []
  
      # conv(padded, bias) -> conv(padded, bias)
      configs.append(
          [
              {"tensor_fqn": "seq.4.weight"},
          ]
      )
      shapes.append((1, 52, 24, 24))
  
      # conv(no bias, no pad) -> conv(padded, bias)
      configs.append(
          [
              {"tensor_fqn": "seq.2.weight"},
          ]
      )
      shapes.append((1, 52, 24, 24))
  
      # conv(padded, bias) -> conv ( no bias ,no pad)
      configs.append(
          [
              {"tensor_fqn": "seq.0.weight"},
          ]
      )
      shapes.append((1, 52, 24, 24))
      # conv(pad, bias) -> conv(no pad, bias)
      configs.append(
          [
              {"tensor_fqn": "seq.6.weight"},
          ]
      )
      shapes.append((1, 52, 24, 24))
      # conv(no pad, bias) -> conv(pad, bias)
      configs.append(
          [
              {"tensor_fqn": "seq.8.weight"},
          ]
      )
      shapes.append((1, 52, 24, 24))
  
      for device in DEVICES:
          x = torch.ones((1, 1, 28, 28), device=device)
          for also_prune_bias in [True, False]:
              for config, shape in zip(configs, shapes):
                  self._test_conv2d_on_device(
                      Conv2dPadBias(),
                      config,
                      x,
                      shape,
                      torch.device(device),
                      also_prune_bias,
                  )
  
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 23 for calling the TestBaseStructuredSparsifier obj's underlying member method code with example usage: obj.test_prune_conv2d_padding_conv2d() without return types