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_5d() -> None:
      input = np.array(
          [
              [
                  [
                      1,
                      6,
                      11,
                      16,
                      21,
                      26,
                      31,
                      36,
                      41,
                      46,
                      51,
                      56,
                  ],  # (1, 10, 12)
                  [2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57],
                  [3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58],
                  [4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59],
                  [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],
                  [61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116],
                  [62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117],
                  [63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118],
                  [64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119],
                  [65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120],
              ]
          ]
      ).astype(np.float32)
      image_shape = np.array([3, 4, 5]).astype(np.int64)
      block_shape = np.array([1, 1, 5]).astype(np.int64)
  
      output = np.array(
          [
              [
                  [
                      [
                          [1, 2, 3, 4, 5],  # (1, 2, 3, 4, 5)
                          [6, 7, 8, 9, 10],
                          [11, 12, 13, 14, 15],
                          [16, 17, 18, 19, 20],
                      ],
                      [
                          [21, 22, 23, 24, 25],
                          [26, 27, 28, 29, 30],
                          [31, 32, 33, 34, 35],
                          [36, 37, 38, 39, 40],
                      ],
                      [
                          [41, 42, 43, 44, 45],
                          [46, 47, 48, 49, 50],
                          [51, 52, 53, 54, 55],
                          [56, 57, 58, 59, 60],
                      ],
                  ],
                  [
                      [
                          [61, 62, 63, 64, 65],
                          [66, 67, 68, 69, 70],
                          [71, 72, 73, 74, 75],
                          [76, 77, 78, 79, 80],
                      ],
                      [
                          [81, 82, 83, 84, 85],
                          [86, 87, 88, 89, 90],
                          [91, 92, 93, 94, 95],
                          [96, 97, 98, 99, 100],
                      ],
                      [
                          [101, 102, 103, 104, 105],
                          [106, 107, 108, 109, 110],
                          [111, 112, 113, 114, 115],
                          [116, 117, 118, 119, 120],
                      ],
                  ],
              ]
          ]
      ).astype(np.float32)
  
      node = onnx.helper.make_node(
          "Col2Im", ["input", "image_shape", "block_shape"], ["output"]
      )
      expect(
          node,
          inputs=[input, image_shape, block_shape],
          outputs=[output],
          name="test_col2im_5d",
      )
  
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 5 for calling the Col2Im obj's underlying member method code with example usage: obj.export_col2im_5d() without return types