code: def test_builder_to_backend_stress(
      self, compute_unit, backend, rank, 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=-1.0, rand_max=1.0
      ).astype(x_dtype)
      input_placeholders = {
          "x": mb.placeholder(shape=shape, dtype=x_builtin_dtype)
      }
      input_values = {"x": x_val}
  
      def build(x):
          return [mb.l2_norm(x=x, epsilon=param_dtype(epsilon))]
  
      output = TestNormalizationL2Norm._compute_l2_norm(x_val, epsilon)
      expected_output_types = [list(output.shape) + [x_builtin_dtype]]
      expected_outputs = [output]
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          compute_unit=compute_unit,
          backend=backend,
      )
  
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 TestNormalizationL2Norm obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(compute_unit, backend, rank, epsilon, x_param_dtype) and returns: list