code: def test_builder_to_backend_stress(
      self,
      use_cpu_only,
      backend,
      conv_dim,
      padding,
      DHWKdKhKw,
      stride,
      dilation,
      has_bias,
      groups,
      test_symbolic,
      test_output_shape,
  ):
      D, H, W, Kd, Kh, Kw = DHWKdKhKw
      N, C_in, C_out = 1, 1 * groups, 2 * groups
  
      import torch
      import torch.nn as nn
  
      isDeconv1d = conv_dim == "conv1d"
      isDeconv2d = conv_dim == "conv2d"
  
      if isDeconv1d:
          strides = [stride[0]]
          dilations = [dilation[0]]
          kernels = [Kh]
          m = nn.ConvTranspose1d(
              C_in,
              C_out,
              kernels,
              stride=strides,
              dilation=dilations,
              bias=has_bias,
              groups=groups,
              padding=padding[0],
          )
          input_shape = [N, C_in, H]
          paddings = [padding[0], padding[0]]
  
      elif isDeconv2d:
          strides = [stride[0], stride[1]]
          dilations = [dilation[0], dilation[1]]
          kernels = [Kh, Kw]
          m = nn.ConvTranspose2d(
              C_in,
              C_out,
              kernels,
              stride=strides,
              dilation=dilations,
              bias=has_bias,
              groups=groups,
              padding=(padding[0], padding[1]),
          )
          input_shape = [N, C_in, H, W]
          paddings = [padding[0], padding[0], padding[1], padding[1]]
      else:
          strides = [stride[0], stride[1], stride[2]]
          dilations = [dilation[0], dilation[1], dilation[2]]
          kernels = [Kd, Kh, Kw]
          m = nn.ConvTranspose3d(
              C_in,
              C_out,
              kernels,
              stride=strides,
              dilation=dilations,
              bias=has_bias,
              groups=groups,
              padding=padding,
          )
          input_shape = [N, C_in, D, H, W]
          paddings = [
              padding[0],
              padding[0],
              padding[1],
              padding[1],
              padding[2],
              padding[2],
          ]
  
      wts = m.state_dict()
      weight = wts["weight"].detach().numpy()
      bias = wts["bias"].detach().numpy() if has_bias else None
  
      # Reshape to CoreML format
      # PyTorch weight format: C_in, C_out, H, W
      # MIL weight format: C_out, C_in, H, W
      if isDeconv1d:
          weight = np.transpose(weight, [1, 0, 2])
      elif isDeconv2d:
          weight = np.transpose(weight, [1, 0, 2, 3])
      else:
          weight = np.transpose(weight, [1, 0, 2, 3, 4])
  
      input = torch.randn(*input_shape)
      output = m(input)
      output = output.detach().numpy()
      input = input.detach().numpy()
  
      output_shape = list(output.shape)
      if test_symbolic:
          # For symbolic input test
          # Make Batch Size and input channel as symbolic
          symbolic_batch_size = get_new_symbol()
          input_shape[0] = symbolic_batch_size
          output_shape[0] = symbolic_batch_size
  
      expected_output_types = tuple(output_shape[:]) + (types.fp32,)
      expected_outputs = [output]
  
      input_placeholders = {"x": mb.placeholder(shape=input_shape)}
      input_values = {"x": input}
  
      def build(x):
          arguments = {
              "x": x,
              "weight": weight,
              "pad": paddings,
              "pad_type": "custom",
              "strides": strides,
              "dilations": dilations,
              "groups": groups,
          }
          if has_bias:
              arguments["bias"] = bias
          if test_output_shape:
              arguments["output_shape"] = output.shape[2:]
          return mb.conv_transpose(**arguments)
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          use_cpu_only=use_cpu_only,
          frontend_only=False,
          backend=backend,
      )
  
imports: from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.mil import get_new_symbol
  from coremltools.converters.mil.testing_reqs import *
  from .testing_utils import run_compare_builder
label_desc: Use image node_id 1 for calling the TestConvTranspose obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(use_cpu_only, backend, conv_dim, padding, DHWKdKhKw, stride, dilation, has_bias, groups, test_symbolic, test_output_shape) and returns: mb