text: def test(name, x, scale, offset, mean, variance, is_training, data_format, epsilon, output,
           x_data, scale_data, offset_data, mean_data, variance_data, output_data ):
      model = Model().Operation("FUSED_BATCH_NORM_V3_EX",
                                x, scale, offset, mean, variance,              # inputs
                                is_training, data_format, epsilon).To(output)  # param
      example = Example({
          x: x_data,
          scale: scale_data,
          offset: offset_data,
          mean: mean_data,
          variance: variance_data,
          output: output_data,
      }, model=model, name=name)
  
  test(
      name = 'fusedbatchnorm_1141',
      x = Input("input0", "TENSOR_FLOAT32", "{1,1,4,1}"),
      scale = Input("input1", "TENSOR_FLOAT32", "{1}"),
      offset = Input("input2", "TENSOR_FLOAT32", "{1}"),
      mean = Input("inputr3", "TENSOR_FLOAT32", "{1}"),
      variance = Input("input4", "TENSOR_FLOAT32", "{1}"),
      is_training = Parameter("is_training", "TENSOR_BOOL8", "{1}", [1]), # true
      data_format = Parameter("data_format", "TENSOR_QUANT8_ASYMM", "{4}, 1.0, 0",
                              [78, 72, 87, 67]), # NHWC: nnapi always assumes channel-last layout
      epsilon = Parameter("epsilon", "TENSOR_FLOAT32", "{1}", [0]),
      x_data=[0., 1., -1., 0. ],
      scale_data=[1.],
      offset_data=[0.],
      mean_data=[0.],
      variance_data=[1.],
      output=Output("output0", "TENSOR_FLOAT32", "{1,1,4,1}"),
      output_data=[0., 1.4142135381698608 , -1.4142135381698608, 0 ],
  )
  