code: def export_conv_with_strides() -> None:
      x = np.array(
          [
              [
                  [
                      [
                          0.0,
                          1.0,
                          2.0,
                          3.0,
                          4.0,
                      ],  # (1, 1, 7, 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],
                      [25.0, 26.0, 27.0, 28.0, 29.0],
                      [30.0, 31.0, 32.0, 33.0, 34.0],
                  ]
              ]
          ]
      ).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 strides=2 and padding
      node_with_padding = onnx.helper.make_node(
          "Conv",
          inputs=["x", "W"],
          outputs=["y"],
          kernel_shape=[3, 3],
          pads=[1, 1, 1, 1],
          strides=[
              2,
              2,
          ],  # Default values for other attributes: dilations=[1, 1], groups=1
      )
      y_with_padding = np.array(
          [
              [
                  [
                      [12.0, 27.0, 24.0],  # (1, 1, 4, 3) output tensor
                      [63.0, 108.0, 81.0],
                      [123.0, 198.0, 141.0],
                      [112.0, 177.0, 124.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      expect(
          node_with_padding,
          inputs=[x, W],
          outputs=[y_with_padding],
          name="test_conv_with_strides_padding",
      )
  
      # Convolution with strides=2 and no padding
      node_without_padding = onnx.helper.make_node(
          "Conv",
          inputs=["x", "W"],
          outputs=["y"],
          kernel_shape=[3, 3],
          pads=[0, 0, 0, 0],
          strides=[
              2,
              2,
          ],  # Default values for other attributes: dilations=[1, 1], groups=1
      )
      y_without_padding = np.array(
          [
              [
                  [
                      [54.0, 72.0],  # (1, 1, 3, 2) output tensor
                      [144.0, 162.0],
                      [234.0, 252.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      expect(
          node_without_padding,
          inputs=[x, W],
          outputs=[y_without_padding],
          name="test_conv_with_strides_no_padding",
      )
  
      # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
      node_with_asymmetric_padding = onnx.helper.make_node(
          "Conv",
          inputs=["x", "W"],
          outputs=["y"],
          kernel_shape=[3, 3],
          pads=[1, 0, 1, 0],
          strides=[
              2,
              2,
          ],  # Default values for other attributes: dilations=[1, 1], groups=1
      )
      y_with_asymmetric_padding = np.array(
          [
              [
                  [
                      [21.0, 33.0],  # (1, 1, 4, 2) output tensor
                      [99.0, 117.0],
                      [189.0, 207.0],
                      [171.0, 183.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      expect(
          node_with_asymmetric_padding,
          inputs=[x, W],
          outputs=[y_with_asymmetric_padding],
          name="test_conv_with_strides_and_asymmetric_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 2 for calling the Conv obj's underlying member method code with example usage: obj.export_conv_with_strides() without return types