code: def test_builder_to_backend_smoke(self, use_cpu_only, 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]],
              ]
          ],
          dtype=np.float32,
      )
      mean_val = np.array([9.0, 6.0, 3.0], dtype=np.float32)
      variance_val = np.array([6.0, 1.0, 7.0], dtype=np.float32)
      gamma_val = np.array([1.0, 1.0, 1.0], dtype=np.float32)
      beta_val = np.array([1.0, 3.0, 0.0], dtype=np.float32)
      input_placeholders = {"x": mb.placeholder(shape=x_val.shape)}
      input_values = {"x": x_val}
  
      def build(x):
          return [
              mb.batch_norm(x=x, mean=mean_val, variance=variance_val),
              mb.batch_norm(
                  x=x,
                  mean=mean_val,
                  variance=variance_val,
                  gamma=gamma_val,
                  beta=beta_val,
                  epsilon=1e-4,
              ),
          ]
  
      expected_output_types = [
          (1, 3, 2, 2, types.fp32),
          (1, 3, 2, 2, types.fp32),
      ]
      expected_outputs = [
          np.array(
              [
                  [
                      [
                          [-10.206199, 1.6329918],
                          [0.8164959, -10.206199],
                      ],
                      [[6.999965, -20.999895], [6.999965, 2.9999852]],
                      [[-4.53557, -2.6457493], [-3.4016776, 0.0]],
                  ]
              ],
              dtype=np.float32,
          ),
          np.array(
              [
                  [
                      [[-9.206122, 2.6329796], [1.8164899, -9.206122]],
                      [[9.99965, -17.998951], [9.99965, 5.9998503]],
                      [[-4.535541, -2.6457324], [-3.4016557, 0.0]],
                  ]
              ],
              dtype=np.float32,
          ),
      ]
  
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          use_cpu_only=use_cpu_only,
          backend=backend,
      )
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/mil/mil/ops/tests/test_normalization.py
imports: from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.testing_reqs import *
  from .testing_utils import run_compare_builder
label_desc: Use image node_id 1 for calling the TestNormalizationBatchNorm obj's underlying member method code with example usage: obj.test_builder_to_backend_smoke(use_cpu_only, backend) and returns: list