code: def testByLeNet(image_shape):
      print("\nLeNet output shape test:")
      print("input_image_shape is : ", image_shape)
      convLay1 = [5, 5]
      WLay1 = [6, -1, 5, 5]
      outputLay1 = inference_shape["Conv"](
          image_shape, WLay1, kernel_shape=convLay1
      )
      print("1st Lay output shape is : ", outputLay1)
  
      poolLay2 = [2, 2]
      stridesLay2 = [2, 2]
      outputLay2 = inference_shape["AveragePool"](
          outputLay1, strides=stridesLay2
      )
      print("2nd Lay output shape is : ", outputLay2)
  
      convLay3 = [5, 5]
      WLay3 = [16, -1, 5, 5]
      outputLay3 = inference_shape["Conv"](
          outputLay2, WLay3, kernel_shape=convLay3
      )
      print("3rd Lay output shape is : ", outputLay3)
  
      poolLay4 = [2, 2]
      stridesLay4 = [2, 2]
      outputLay4 = inference_shape["AveragePool"](
          outputLay3, strides=stridesLay4
      )
      print("4th Lay output shape is : ", outputLay4)
  
      convLay5 = [5, 5]
      WLay5 = [120, -1, 5, 5]
      outputLay5 = inference_shape["Conv"](outputLay4, WLay5)
      print("5th Lay output shape is : ", outputLay5)
  
      outputLay5Flatten = inference_shape["Flatten"](outputLay5)
      WLay6 = [-1, 84]
      outputLay6 = inference_shape["FC"](outputLay5Flatten, WLay6)
      print("6th Lay output shape is : ", outputLay6)
  
      WLay7 = [-1, 10]
      outputLay7 = inference_shape["FC"](outputLay6, WLay7)
      print("7th Lay output shape is : ", outputLay7)
      return outputLay7
  
file_path: MMdnn/mmdnn/conversion/onnx/shape_inference.py
label_desc: Use image node_id 12 for calling a global function with example usage: testByLeNet(image_shape) and returns: outputLay7