code: def export_default() -> None:
      alpha = 0.0001
      beta = 0.75
      bias = 1.0
      nsize = 3
      node = onnx.helper.make_node(
          "LRN", inputs=["x"], outputs=["y"], size=3
      )
      x = np.random.randn(5, 5, 5, 5).astype(np.float32)
      square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
      for n, c, h, w in np.ndindex(x.shape):
          square_sum[n, c, h, w] = sum(
              x[
                  n,
                  max(0, c - int(math.floor((nsize - 1) / 2))) : min(
                      5, c + int(math.ceil((nsize - 1) / 2)) + 1
                  ),
                  h,
                  w,
              ]
              ** 2
          )
      y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
      expect(node, inputs=[x], outputs=[y], name="test_lrn_default")
  
file_path: onnx/onnx/backend/test/case/node/lrn.py
imports: import math
  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 2 for calling the LRN obj's underlying member method code with example usage: obj.export_default() without return types