code: def test_conv1d_with_relu_fc(self):
      batch_size_list = range(1, 3)
      input_channels_per_group_list = range(10, 12)
      width_list = range(10, 12)
      output_channels_per_group_list = range(10, 12)
      groups_list = range(1, 3)
      kernel_list = range(1, 4)
      stride_list = range(1, 3)
      padding_list = range(0, 3)
      dilation_list = range(1, 3)
      output_features_list = range(1, 3)
  
      for hparams in itertools.product(
          batch_size_list,
          input_channels_per_group_list,
          width_list,
          output_channels_per_group_list,
          groups_list,
          kernel_list,
          stride_list,
          padding_list,
          dilation_list,
          output_features_list,
      ):
          (
              batch_size,
              input_channels_per_group,
              width,
              output_channels_per_group,
              groups,
              kernel,
              stride,
              padding,
              dilation,
              output_features,
          ) = hparams
  
          input_channels = input_channels_per_group * groups
          output_channels = output_channels_per_group * groups
          conv_weight_shape = (
              output_channels,
              input_channels_per_group,
              kernel,
          )
          conv_bias_shape = output_channels
          conv_output_width = (
              int(
                  (width + 2 * padding - dilation * (kernel - 1) - 1)
                  / stride
              )
              + 1
          )
          fc_weight_shape = (
              output_features,
              output_channels * conv_output_width,
          )
          fc_bias_shape = output_features
  
          class Net(torch.nn.Module):
              def __init__(self):
                  super().__init__()
                  self.conv_weight = torch.nn.Parameter(
                      torch.rand(conv_weight_shape), requires_grad=False
                  )
                  self.conv_bias = torch.nn.Parameter(
                      torch.rand(conv_bias_shape), requires_grad=False
                  )
                  self.stride = stride
                  self.padding = padding
                  self.dilation = dilation
                  self.groups = groups
  
                  self.fc_weight = torch.nn.Parameter(
                      torch.rand(fc_weight_shape), requires_grad=False
                  )
                  self.fc_bias = torch.nn.Parameter(
                      torch.rand(fc_bias_shape), requires_grad=False
                  )
  
              def forward(self, x):
                  x = F.conv1d(
                      x,
                      self.conv_weight,
                      self.conv_bias,
                      self.stride,
                      self.padding,
                      self.dilation,
                      self.groups,
                  )
                  x = F.relu(x)
                  x = x.view(x.size(0), -1)
                  x = F.linear(x, self.fc_weight, self.fc_bias)
                  return x
  
          data_shape = (batch_size, input_channels, width)
          pattern_count_transformed_map = {
              "Tensor = aten::conv1d": -1,
              "Tensor = aten::conv2d": 1,
          }
          pattern_count_optimized_map = {
              "Tensor = aten::conv1d": -1,
              "Tensor = aten::conv2d": -1,
              "prepacked::conv2d_clamp_prepack": -1,
              "prepacked::conv2d_clamp_run": 1,
          }
          TestXNNPACKConv1dTransformPass.validate_transform_conv1d_to_conv2d(
              Net(),
              pattern_count_transformed_map,
              pattern_count_optimized_map,
              data_shape,
          )
  
imports: import unittest
  import torch
  import torch.backends.xnnpack
  from torch.nn import functional
  from torch.utils.mobile_optimizer import optimize_for_mobile
  from torch.testing import FileCheck
  import torch.testing._internal.hypothesis_utils
  from torch.testing._internal.common_utils import TestCase, run_tests, slowTest
  from hypothesis import given, assume
  from hypothesis import strategies
  import io
  import itertools
  from torch.testing._internal.common_utils import IS_FBCODE, TEST_WITH_TSAN
label_desc: Use image node_id 3 for calling the TestXNNPACKConv1dTransformPass obj's underlying member method code with example usage: obj.test_conv1d_with_relu_fc() and returns: x