code: def test_builder_to_backend_stress_numpy(
      self,
      compute_unit,
      backend,
      rank_and_axes,
      epsilon,
      provides_gamma_beta,
  ):
      if (
          backend.backend == "mlprogram"
          and backend.precision == "fp16"
          and compute_unit != ct.ComputeUnit.CPU_ONLY
      ):
          pytest.xfail(
              "rdar://80662357 ([GPU failures] LayerNorm FP16 tests failing on GPU with numerical errors)"
          )
  
      if (
          backend.backend == "neuralnetwork"
          and compute_unit != ct.ComputeUnit.CPU_ONLY
          and platform.machine() == "arm64"
      ):
          pytest.xfail(
              "rdar://98015195 ([M1 native tests] Some MIL unittests are failing on M1 native)"
          )
  
      rank, axes = rank_and_axes
      shape = np.random.randint(low=2, high=6, size=rank)
      x_val = random_gen(shape=shape, rand_min=-100.0, rand_max=100.0)
      input_placeholders = {"x": mb.placeholder(shape=x_val.shape)}
      input_values = {"x": x_val}
  
      gamma, beta = None, None
  
      if provides_gamma_beta:
          positive_axes = [
              axis + rank if axis < 0 else axis for axis in axes
          ]
          normalized_shape = [
              shape[i] for i in range(rank) if i in positive_axes
          ]
          gamma = random_gen(
              shape=normalized_shape, rand_min=-100, rand_max=100
          )
          beta = random_gen(
              shape=normalized_shape, rand_min=-100, rand_max=100
          )
  
      def build(x):
          return [
              mb.layer_norm(
                  x=x,
                  axes=axes,
                  epsilon=epsilon,
                  gamma=gamma,
                  beta=beta,
              )
          ]
  
      output = TestNormalizationLayerNorm._np_layer_norm(
          x=x_val, axes=axes, epsilon=epsilon, gamma=gamma, beta=beta
      )
      expected_output_types = [tuple(output.shape) + (types.fp32,)]
      expected_outputs = [output]
  
      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,
      )
  
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 6 for calling the TestNormalizationLayerNorm obj's underlying member method code with example usage: obj.test_builder_to_backend_stress_numpy(compute_unit, backend, rank_and_axes, epsilon, provides_gamma_beta) and returns: list