code: def export() -> None:
      node = onnx.helper.make_node(
          "MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
      )
  
      input_data = np.array(
          [
              [
                  [[0.8439683], [0.5665144], [0.05836735]],
                  [[0.02916367], [0.12964272], [0.5060197]],
                  [[0.79538304], [0.9411346], [0.9546573]],
              ],
              [
                  [[0.17730942], [0.46192095], [0.26480448]],
                  [[0.6746842], [0.01665257], [0.62473077]],
                  [[0.9240844], [0.9722341], [0.11965699]],
              ],
              [
                  [[0.41356155], [0.9129373], [0.59330076]],
                  [[0.81929934], [0.7862604], [0.11799799]],
                  [[0.69248444], [0.54119414], [0.07513223]],
              ],
          ],
          dtype=np.float32,
      )
  
      # Calculate expected output data
      data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
      data_mean_squared = np.power(data_mean, 2)
      data_squared = np.power(input_data, 2)
      data_squared_mean = np.mean(
          data_squared, axis=(0, 2, 3), keepdims=1
      )
      std = np.sqrt(data_squared_mean - data_mean_squared)
      expected_output = (input_data - data_mean) / (std + 1e-9)
  
      expect(
          node,
          inputs=[input_data],
          outputs=[expected_output],
          name="test_mvn",
      )
  
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 1 for calling the MeanVarianceNormalization obj's underlying member method code with example usage: obj.export() without return types