code: def test_padding_mode_stress(
      self, compute_unit, backend, padding_mode, conv_dim
  ):
      def rotation_tensor(tensor):
          assert tensor.shape[0] == tensor.shape[1] == 1
          tensor = tensor[0][0]
          rank = len(tensor.shape)
          new_tensor = np.copy(np.flip(tensor, axis=tuple(range(rank))))
          return np.expand_dims(new_tensor, axis=(0, 1))
  
      if conv_dim == "conv3d" and padding_mode == "same_lower":
          if backend.backend == "neuralnetwork":
              pytest.skip(
                  "same_lower mode not supported for conv3d in neuralnetwork backend"
              )
  
      if (
          padding_mode == "same_lower"
          and backend.opset_version == ct.target.iOS15
      ):
          pytest.skip("same_lower pad_type not supported iOS15 opset")
  
      batch, in_channels, out_channels = 1, 1, 1
      input_shape = (
          batch,
          in_channels,
          4,
          5,
          6,
      )  # batch, channel, height, width
      kernel_size = (2, 4, 3)
      torch_padding_mode = (
          padding_mode if padding_mode != "same_lower" else "same"
      )
  
      # Get the right shape for each conv_dim
      if conv_dim == "conv1d":
          input_shape = input_shape[:3]
          kernel_size = kernel_size[:1]
      elif conv_dim == "conv2d":
          input_shape = input_shape[:4]
          kernel_size = kernel_size[:2]
  
      # Get the ground truth answer from torch
      if conv_dim == "conv1d":
          m = torch.nn.Conv1d(
              in_channels,
              out_channels,
              kernel_size,
              stride=1,
              padding=torch_padding_mode,
              bias=False,
          )
      elif conv_dim == "conv2d":
          m = torch.nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size,
              stride=1,
              padding=torch_padding_mode,
              bias=False,
          )
      elif conv_dim == "conv3d":
          m = torch.nn.Conv3d(
              in_channels,
              out_channels,
              kernel_size,
              stride=1,
              padding=torch_padding_mode,
              bias=False,
          )
  
      # Original weight / inputs for the torch model
      weight = torch.clone(m.state_dict()["weight"])
      input = torch.randn(*input_shape, dtype=torch.float32)
  
      # Coreml weights / inputs values
      coreml_weight = weight.detach().numpy()
      coreml_input = input.detach().numpy()
  
      if padding_mode == "same_lower":
          # For the same_lower padding mode, we get the ground truth output by doing the following steps
          # (1) Rotate the input value
          # (2) Rotate the kernel value
          # (3) Rotate the torch out
          rotated_input = torch.tensor(
              rotation_tensor(input.detach().numpy()),
              dtype=torch.float32,
          )
          rotated_weight = torch.tensor(
              rotation_tensor(weight.detach().numpy()),
              dtype=torch.float32,
          )
          m.load_state_dict({"weight": rotated_weight}, strict=False)
          output = m(rotated_input).detach().numpy()
          output = rotation_tensor(output)
      else:
          output = m(input).detach().numpy()
  
      output_shape = list(output.shape)
      expected_output_types = tuple(output_shape[:]) + (types.fp32,)
      expected_outputs = [output]
      input_placeholders = {"x": mb.placeholder(shape=input_shape)}
      input_values = {"x": coreml_input}
  
      def build(x):
          arguments = {
              "x": x,
              "weight": coreml_weight,
              "pad_type": padding_mode,
          }
          return mb.conv(**arguments)
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          compute_unit=compute_unit,
          backend=backend,
      )
  
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 2 for calling the TestConv obj's underlying member method code with example usage: obj.test_padding_mode_stress(compute_unit, backend, padding_mode, conv_dim) and returns: np, mb