code: def export() -> None:
      x = np.random.randn(3, 4, 2, 2).astype(np.float32)
      num_groups = 2
      scale = np.random.randn(num_groups).astype(np.float32)
      bias = np.random.randn(num_groups).astype(np.float32)
      y = _group_normalization(x, num_groups, scale, bias).astype(
          np.float32
      )
  
      node = onnx.helper.make_node(
          "GroupNormalization",
          inputs=["x", "scale", "bias"],
          outputs=["y"],
          num_groups=num_groups,
      )
  
      expect(
          node,
          inputs=[x, scale, bias],
          outputs=[y],
          name="test_group_normalization_example",
      )
  
      x = np.random.randn(3, 4, 2, 2).astype(np.float32)
      num_groups = 2
      scale = np.random.randn(num_groups).astype(np.float32)
      bias = np.random.randn(num_groups).astype(np.float32)
      epsilon = 1e-2
      y = _group_normalization(
          x, num_groups, scale, bias, epsilon
      ).astype(np.float32)
  
      node = onnx.helper.make_node(
          "GroupNormalization",
          inputs=["x", "scale", "bias"],
          outputs=["y"],
          epsilon=epsilon,
          num_groups=num_groups,
      )
  
      expect(
          node,
          inputs=[x, scale, bias],
          outputs=[y],
          name="test_group_normalization_epsilon",
      )
  
file_path: onnx/onnx/backend/test/case/node/groupnormalization.py
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 GroupNormalization obj's underlying member method code with example usage: obj.export() without return types