code: def test_builder_to_backend_smoke_with_gamma_and_beta(
      self, compute_unit, backend, 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.")
  
      x_val = np.array(
          [
              [
                  [[-16.0, 13.0], [11.0, 16.0]],
                  [[13.0, 15.0], [13.0, 9.0]],
                  [[-9.0, 4.0], [-6.0, 3.0]],
              ],
              [
                  [[-5.0, 1.0], [12.0, 3.0]],
                  [[0.0, 9.0], [2.0, -8.0]],
                  [[2.0, 5.0], [10.0, 0.0]],
              ],
          ],
          dtype=x_dtype,
      )
      gamma_val = np.array([-9.0, 3.2, 1.3], dtype=param_dtype)
      beta_val = np.array([-0.8, 3.4, 1.2], dtype=param_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,
              gamma=gamma_val,
              beta=beta_val,
              epsilon=param_dtype(1e-2),
          )
  
      expected_output_types = [(2, 3, 2, 2, x_builtin_dtype)]
      expected_outputs = [
          np.array(
              [
                  [
                      [
                          [14.63721807, -5.71184211],
                          [-4.30845865, -7.8169173],
                      ],
                      [
                          [4.1333588, 7.06679399],
                          [4.1333588, -1.73351158],
                      ],
                      [
                          [-0.42112757, 2.58953791],
                          [0.27364139, 2.35794826],
                      ],
                  ],
                  [
                      [
                          [10.6363473, 1.782401],
                          [-14.44983388, -1.16891443],
                      ],
                      [
                          [3.00381959, 7.75798456],
                          [4.06030069, -1.22210484],
                      ],
                      [
                          [0.42371726, 1.45876091],
                          [3.18383368, -0.26631185],
                      ],
                  ],
              ],
              dtype=np.float32,
          )
      ]
  
      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 2 for calling the TestNormalizationInstanceNorm obj's underlying member method code with example usage: obj.test_builder_to_backend_smoke_with_gamma_and_beta(compute_unit, backend, x_param_dtype) and returns: mb