class_docstr: Col2Im operator with N-dimension support
  
  The tests below can be reproduced in Python using https://github.com/f-dangel/unfoldNd/
class_docstr_tok: ["Col2Im","operator","with","N-dimension","support","The","tests","below","can","be","reproduced","in","Python","using","https",":","\/\/github.com\/f-dangel\/unfoldNd\/"]
code: def export_col2im_pads() -> None:
      input = np.array(
          [
              [
                  [
                      1.0,
                      6.0,
                      11.0,
                      16.0,
                      21.0,
                      26,
                      31,
                      36,
                      41,
                      46,
                      51,
                      56,
                      61,
                      66,
                      71,
                  ],  # (1, 5, 15)
                  [
                      2.0,
                      7.0,
                      12.0,
                      17.0,
                      22.0,
                      27,
                      32,
                      37,
                      42,
                      47,
                      52,
                      57,
                      62,
                      67,
                      72,
                  ],
                  [
                      3.0,
                      8.0,
                      13.0,
                      18.0,
                      23.0,
                      28,
                      33,
                      38,
                      43,
                      48,
                      53,
                      58,
                      63,
                      68,
                      73,
                  ],
                  [
                      4.0,
                      9.0,
                      14.0,
                      19.0,
                      24.0,
                      29,
                      34,
                      39,
                      44,
                      49,
                      54,
                      59,
                      64,
                      69,
                      74,
                  ],
                  [
                      5.0,
                      10.0,
                      15.0,
                      20.0,
                      25.0,
                      30,
                      35,
                      40,
                      45,
                      50,
                      55,
                      60,
                      65,
                      70,
                      75,
                  ],
              ]
          ]
      ).astype(np.float32)
      image_shape = np.array([5, 5]).astype(np.int64)
      block_shape = np.array([1, 5]).astype(np.int64)
  
      output = np.array(
          [
              [
                  [
                      [8.0, 21.0, 24.0, 27.0, 24.0],  # (1, 1, 5, 5)
                      [38.0, 66.0, 69.0, 72.0, 54.0],
                      [68.0, 111.0, 114.0, 117.0, 84.0],
                      [98.0, 156.0, 159.0, 162.0, 114.0],
                      [128.0, 201.0, 204.0, 207.0, 144.0],
                  ]
              ]
          ]
      ).astype(np.float32)
  
      node = onnx.helper.make_node(
          "Col2Im",
          ["input", "image_shape", "block_shape"],
          ["output"],
          pads=[0, 1, 0, 1],
      )
      expect(
          node,
          inputs=[input, image_shape, block_shape],
          outputs=[output],
          name="test_col2im_pads",
      )
  
file_path: onnx/onnx/backend/test/case/node/col2im.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 3 for calling the Col2Im obj's underlying member method code with example usage: obj.export_col2im_pads() without return types