code: def test_builder_to_backend_stress(
      self, rank, compute_unit, backend, epsilon, x_param_dtype
  ):
      x_dtype, param_dtype = x_param_dtype
      x_builtin_dtype = types.numpy_type_to_builtin_type(x_dtype)
  
      if x_dtype == np.float16 and backend.backend == "neuralnetwork":
          pytest.skip("No need to test fp16 for neuralnetwork backend.")
  
      shape = np.random.randint(low=2, high=6, size=rank)
      x_val = random_gen(
          shape=shape, rand_min=-100.0, rand_max=100.0
      ).astype(x_dtype)
      input_placeholders = {
          "x": mb.placeholder(shape=x_val.shape, dtype=x_builtin_dtype)
      }
      input_values = {"x": x_val}
  
      def build(x):
          return mb.instance_norm(x=x, epsilon=param_dtype(epsilon))
  
      layer = (
          torch.nn.InstanceNorm2d
          if rank == 4
          else torch.nn.InstanceNorm1d
      )
      torch_op = layer(num_features=shape[1], eps=epsilon)
      # PyTorch's batch_norm op is not implemented for fp16, so need to cast to fp32 first.
      expected_outputs = [
          torch_op(torch.as_tensor(x_val.astype(np.float32))).numpy()
      ]
      expected_output_types = [
          o.shape[:] + (x_builtin_dtype,) for o in expected_outputs
      ]
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          compute_unit=compute_unit,
          backend=backend,
          atol=1e-3,
          rtol=1e-4,
          also_compare_shapes=True,
      )
  
imports: import itertools
  import platform
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_TF_2, _HAS_TORCH, MSG_TF2_NOT_FOUND, MSG_TORCH_NOT_FOUND
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, get_new_symbol, types
  from coremltools.converters.mil.mil.ops.tests.iOS14 import backends
  from coremltools.converters.mil.mil.ops.tests.testing_utils import UNK_SYM, construct_inputs_from_placeholders, 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 TestNormalizationInstanceNorm obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(rank, compute_unit, backend, epsilon, x_param_dtype) and returns: mb