code: def test_builder_to_backend_stress(
      self,
      compute_unit,
      backend,
      input_shape,
      scale_factor,
      align_corners,
      recompute_scale_factor,
  ):
      scale_factor_height, scale_factor_width = scale_factor
      _, _, height, width = input_shape
      height = height * scale_factor_height
      width = width * scale_factor_width
      is_h_float = height - np.floor(height) > 0.001
      is_w_float = width - np.floor(width) > 0.001
  
      # Currently, MIL is not supporting recompute_scale_factor=False + align_corners=False
      # with fractional output size
      if (
          not recompute_scale_factor
          and not align_corners
          and (is_h_float or is_w_float)
      ):
          pytest.xfail(
              "rdar://81124053 (Support recompute_scale_factor)"
          )
  
      def _get_torch_upsample_prediction(
          x,
          scale_factor=(2, 2),
          align_corners=False,
          recompute_scale_factor=True,
      ):
          x = torch.from_numpy(x)
          out = torch.nn.functional.interpolate(
              x,
              scale_factor=scale_factor,
              mode="bilinear",
              align_corners=align_corners,
              recompute_scale_factor=recompute_scale_factor,
          )
          return out.numpy()
  
      x = random_gen(input_shape, rand_min=-100, rand_max=100)
      torch_pred = _get_torch_upsample_prediction(
          x,
          scale_factor=scale_factor,
          align_corners=align_corners,
          recompute_scale_factor=recompute_scale_factor,
      )
  
      input_placeholder_dict = {"x": mb.placeholder(shape=x.shape)}
      input_value_dict = {"x": x}
  
      def build_upsample(x):
          return mb.upsample_bilinear(
              x=x,
              scale_factor_height=scale_factor[0],
              scale_factor_width=scale_factor[1],
              align_corners=align_corners,
          )
  
      expected_output_type = torch_pred.shape + (types.fp32,)
      run_compare_builder(
          build_upsample,
          input_placeholder_dict,
          input_value_dict,
          expected_output_type,
          torch_pred,
          compute_unit=compute_unit,
          backend=backend,
          rtol=0.5,
      )
  
imports: import functools
  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 mark_api_breaking, 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 TestUpsampleBilinear obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(compute_unit, backend, input_shape, scale_factor, align_corners, recompute_scale_factor) and returns: out, mb