code: def test_fused_batch_norm(self, compute_unit, func, backend, epsilon):
      input_shape = np.random.randint(low=1, high=4, size=4)
      attr_shape = [list(input_shape)[-1]]
  
      m = random_gen(shape=attr_shape, rand_min=-1.0, rand_max=1.0)
      v = random_gen(shape=attr_shape, rand_min=0.0, rand_max=10.0)
      o = random_gen(shape=attr_shape, rand_min=1.0, rand_max=10.0)
      s = random_gen(shape=attr_shape, rand_min=-1.0, rand_max=1.0)
  
      @make_tf_graph([input_shape])
      def build_model(x):
          return func(
              x=x,
              scale=s,
              offset=o,
              mean=m,
              variance=v,
              epsilon=epsilon,
              is_training=False,
          )[0]
  
      model, inputs, outputs = build_model
      input_values = [random_gen(shape=input_shape)]
      input_dict = dict(zip(inputs, input_values))
  
      TensorFlowBaseTest.run_compare_tf(
          model,
          input_dict,
          outputs,
          compute_unit=compute_unit,
          backend=backend,
          atol=1e-2,
          rtol=1e-3,
      )
  
imports: import itertools
  import numpy
  import pytest
  import coremltools
  from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.frontend.tensorflow.test.testing_utils import TensorFlowBaseTest
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import TensorFlow2BaseTest
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import make_tf2_graph
  from coremltools.converters.mil.testing_utils import random_gen
label_desc: Use image node_id 1 for calling the TestNormalizationTF2 obj's underlying member method code with example usage: obj.test_fused_batch_norm(compute_unit, func, backend, epsilon) and returns: func