code: def test_conv2d_transpose(
      self,
      batch_size,
      input_channels_per_group,
      height,
      width,
      output_channels_per_group,
      groups,
      kernel_h,
      kernel_w,
      stride_h,
      stride_w,
      pad_h,
      pad_w,
      output_pad_h,
      output_pad_w,
      dilation,
      use_bias,
      format,
  ):
      input_channels = input_channels_per_group * groups
      output_channels = output_channels_per_group * groups
      kernels = (kernel_h, kernel_w)
      strides = (stride_h, stride_w)
      paddings = (pad_h, pad_w)
      output_paddings = (output_pad_h, output_pad_w)
      dilations = (dilation, dilation)
      assume(
          height + 2 * paddings[0]
          >= dilations[0] * (kernels[0] - 1) + 1
      )
      assume(
          width + 2 * paddings[1] >= dilations[1] * (kernels[1] - 1) + 1
      )
      assume((output_pad_h < stride_h) and (output_pad_h < dilation))
      assume((output_pad_w < stride_w) and (output_pad_w < dilation))
  
      input_data = torch.rand(
          (batch_size, input_channels, height, width)
      )
      if format is not None:
          input_data = input_data.contiguous(memory_format=format)
      weight = torch.rand(
          (
              input_channels,
              output_channels_per_group,
              kernel_h,
              kernel_w,
          )
      )
      bias = None
      if use_bias:
          bias = torch.rand(output_channels)
  
      # Note that groups/dilation is in reverse order from conv2d
      ref_result = F.conv_transpose2d(
          input_data,
          weight,
          bias,
          strides,
          paddings,
          output_paddings,
          groups,
          dilation,
      )
      packed_weight_bias = (
          torch.ops.prepacked.conv2d_transpose_clamp_prepack(
              weight,
              bias,
              strides,
              paddings,
              output_paddings,
              dilations,
              groups,
          )
      )
      xnnpack_result = torch.ops.prepacked.conv2d_transpose_clamp_run(
          input_data, packed_weight_bias
      )
      torch.testing.assert_close(
          ref_result.contiguous(),
          xnnpack_result.contiguous(),
          rtol=1e-2,
          atol=1e-3,
      )
  
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 4 for calling the TestXNNPACKOps obj's underlying member method code with example usage: obj.test_conv2d_transpose(batch_size, input_channels_per_group, height, width, output_channels_per_group, groups, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, output_pad_h, output_pad_w, dilation, use_bias, format) without return types