code: def test_builder_to_backend_stress(
      self,
      compute_unit,
      backend,
      conv_dim,
      config,
      x_weight_dtype,
  ):
      padding = config["padding"]
      DHWKdKhKw = config["DHWKdKhKw"]
      stride = config["stride"]
      dilation = config["dilation"]
      has_bias = config["has_bias"]
      groups = config["groups"]
      symbolic = config["symbolic"]
  
      D, H, W, Kd, Kh, Kw = DHWKdKhKw
      N, C_in, C_out = 1, 1 * groups, 2 * groups
  
      x_dtype, weight_bias_dtype = x_weight_dtype
      x_builtin_dtype = types.numpy_type_to_builtin_type(x_dtype)
  
      isConv1d = conv_dim == "conv1d"
      isConv2d = conv_dim == "conv2d"
  
      if isConv1d:
          strides = [stride[0]]
          dilations = [dilation[0]]
          kernels = [Kh]
          m = nn.Conv1d(
              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 isConv2d:
          strides = [stride[0], stride[1]]
          dilations = [dilation[0], dilation[1]]
          kernels = [Kh, Kw]
          m = nn.Conv2d(
              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.Conv3d(
              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().astype(weight_bias_dtype)
      bias = (
          wts["bias"].detach().numpy().astype(weight_bias_dtype)
          if has_bias
          else None
      )
  
      # PyTorch and CoreML weight format is same
      # PyTorch weight format: C_out, C_in, H, W
      # MIL weight format: C_out, C_in, H, W
  
      input = random_gen(input_shape)
      input = torch.Tensor(input)
      output = m(input)
      output = output.detach().numpy().astype(x_dtype)
      input = input.detach().numpy().astype(x_dtype)
  
      output_shape = list(output.shape)
      if 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[:]) + (
          x_builtin_dtype,
      )
      expected_outputs = [output]
  
      input_placeholders = {
          "x": mb.placeholder(shape=input_shape, dtype=x_builtin_dtype)
      }
      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
          return mb.conv(**arguments)
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          compute_unit=compute_unit,
          backend=backend,
          atol=1e-3
          if x_dtype == np.float16
          and backend.backend == "neuralnetwork"
          else 1e-4,
      )
  
imports: import itertools
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_TORCH, MSG_TORCH_NOT_FOUND
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import get_new_symbol, types
  from coremltools.converters.mil.mil.ops.tests.iOS14 import backends
  from coremltools.converters.mil.mil.ops.tests.testing_utils import run_compare_builder
  from coremltools.converters.mil.testing_reqs import compute_units
  from coremltools.converters.mil.testing_utils import random_gen
label_desc: Use image node_id 3 for calling the TestConv obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(compute_unit, backend, conv_dim, config, x_weight_dtype) and returns: mb