code: def export() -> None:
      x = np.array(
          [
              [
                  [
                      [
                          0.0,
                          1.0,
                          2.0,
                          3.0,
                          4.0,
                      ],  # (1, 1, 5, 5) input tensor
                      [5.0, 6.0, 7.0, 8.0, 9.0],
                      [10.0, 11.0, 12.0, 13.0, 14.0],
                      [15.0, 16.0, 17.0, 18.0, 19.0],
                      [20.0, 21.0, 22.0, 23.0, 24.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      W = np.array(
          [
              [
                  [
                      [
                          1.0,
                          1.0,
                          1.0,
                      ],  # (1, 1, 3, 3) tensor for convolution weights
                      [1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0],
                  ]
              ]
          ]
      ).astype(np.float32)
  
      # Convolution with padding
      node_with_padding = onnx.helper.make_node(
          "Conv",
          inputs=["x", "W"],
          outputs=["y"],
          kernel_shape=[3, 3],
          # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
          pads=[1, 1, 1, 1],
      )
      y_with_padding = np.array(
          [
              [
                  [
                      [
                          12.0,
                          21.0,
                          27.0,
                          33.0,
                          24.0,
                      ],  # (1, 1, 5, 5) output tensor
                      [33.0, 54.0, 63.0, 72.0, 51.0],
                      [63.0, 99.0, 108.0, 117.0, 81.0],
                      [93.0, 144.0, 153.0, 162.0, 111.0],
                      [72.0, 111.0, 117.0, 123.0, 84.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      expect(
          node_with_padding,
          inputs=[x, W],
          outputs=[y_with_padding],
          name="test_basic_conv_with_padding",
      )
  
      # Convolution without padding
      node_without_padding = onnx.helper.make_node(
          "Conv",
          inputs=["x", "W"],
          outputs=["y"],
          kernel_shape=[3, 3],
          # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
          pads=[0, 0, 0, 0],
      )
      y_without_padding = np.array(
          [
              [
                  [
                      [54.0, 63.0, 72.0],  # (1, 1, 3, 3) output tensor
                      [99.0, 108.0, 117.0],
                      [144.0, 153.0, 162.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      expect(
          node_without_padding,
          inputs=[x, W],
          outputs=[y_without_padding],
          name="test_basic_conv_without_padding",
      )
  
file_path: onnx/onnx/backend/test/case/node/conv.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 Conv obj's underlying member method code with example usage: obj.export() without return types