code: def test_insert_pre_packed_linear_before_inline_and_conv_2d_op(self):
      class TestPrepackedLinearBeforeInlineAndConv2dOp(torch.nn.Module):
          def __init__(
              self,
              linear_weight,
              linear_bias,
              conv2d_weight,
              conv2d_bias,
              conv_transpose2d_weight,
              conv_transpose2d_bias,
          ):
              super(
                  TestPrepackedLinearBeforeInlineAndConv2dOp,
                  self,
              ).__init__()
              self.linear_weight = linear_weight.float()
              self.linear_bias = linear_bias.float()
              self.conv2d_weight = conv2d_weight.float()
              self.conv2d_bias = conv2d_bias.float()
              self.conv_transpose2d_weight = (
                  conv_transpose2d_weight.float()
              )
              self.conv_transpose2d_bias = conv_transpose2d_bias.float()
  
          def forward(self, x):
              linear_res = F.linear(
                  x.float(),
                  self.linear_weight,
                  self.linear_bias,
              )
              conv2d_res = F.conv2d(
                  input=linear_res.unsqueeze(dim=0).float(),
                  weight=self.conv2d_weight,
                  bias=self.conv2d_bias,
              )
              return F.conv_transpose2d(
                  input=conv2d_res,
                  weight=self.conv_transpose2d_weight,
                  bias=self.conv_transpose2d_bias,
              )
  
      minibatch = 1
      in_channels = 6
      iH = 4
      iW = 5
      out_channels = 6
      kH = 2
      kW = 3
  
      self.check_replacement(
          model=torch.jit.script(
              TestPrepackedLinearBeforeInlineAndConv2dOp(
                  linear_weight=torch.rand(iW, 3),
                  linear_bias=torch.rand(iW),
                  conv2d_weight=torch.rand(
                      out_channels, in_channels, kH, kW
                  ),
                  conv2d_bias=torch.rand(out_channels),
                  conv_transpose2d_weight=torch.rand(
                      out_channels,
                      in_channels,
                      kH,
                      kW,
                  ),
                  conv_transpose2d_bias=torch.rand(out_channels),
              ),
          ),
          replacements={
              "prepacked::linear_clamp_prepack": "aten::linear",
              "prepacked::linear_clamp_run": "aten::linear",
              "prepacked::conv2d_clamp_prepack": "aten::conv2d",
              "prepacked::conv2d_clamp_run": "aten::conv2d",
              "prepacked::conv2d_transpose_clamp_prepack": "aten::conv_transpose2d",
              "prepacked::conv2d_transpose_clamp_run": "aten::conv_transpose2d",
          },
          jit_pass=torch._C._jit_pass_insert_prepacked_ops,
      )
  
file_path: pytorch/test/jit/test_optimize_for_mobile_preserve_debug_info.py
imports: import torch
  import torch._C
  import torch.nn.functional
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.common_utils import skipIfNoXNNPACK
label_desc: Use image node_id 3 for calling the TestOptimizeForMobilePreserveDebugInfo obj's underlying member method code with example usage: obj.test_insert_pre_packed_linear_before_inline_and_conv_2d_op() and returns: F