code: def export() -> None:
      def _instancenorm_test_mode(x, s, bias, epsilon=1e-5):  # type: ignore
          dims_x = len(x.shape)
          axis = tuple(range(2, dims_x))
          mean = np.mean(x, axis=axis, keepdims=True)
          var = np.var(x, axis=axis, keepdims=True)
          dim_ones = (1,) * (dims_x - 2)
          s = s.reshape(-1, *dim_ones)
          bias = bias.reshape(-1, *dim_ones)
          return s * (x - mean) / np.sqrt(var + epsilon) + bias
  
      # input size: (1, 2, 1, 3)
      x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
      s = np.array([1.0, 1.5]).astype(np.float32)
      bias = np.array([0, 1]).astype(np.float32)
      y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
  
      node = onnx.helper.make_node(
          "InstanceNormalization",
          inputs=["x", "s", "bias"],
          outputs=["y"],
      )
  
      # output size: (1, 2, 1, 3)
      expect(
          node,
          inputs=[x, s, bias],
          outputs=[y],
          name="test_instancenorm_example",
      )
  
      # input size: (2, 3, 4, 5)
      x = np.random.randn(2, 3, 4, 5).astype(np.float32)
      s = np.random.randn(3).astype(np.float32)
      bias = np.random.randn(3).astype(np.float32)
      epsilon = 1e-2
      y = _instancenorm_test_mode(x, s, bias, epsilon).astype(
          np.float32
      )
  
      node = onnx.helper.make_node(
          "InstanceNormalization",
          inputs=["x", "s", "bias"],
          outputs=["y"],
          epsilon=epsilon,
      )
  
      # output size: (2, 3, 4, 5)
      expect(
          node,
          inputs=[x, s, bias],
          outputs=[y],
          name="test_instancenorm_epsilon",
      )
  
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 InstanceNormalization obj's underlying member method code with example usage: obj.export() and returns: unknown