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_dilations() -> None:
      input = np.array(
          [
              [
                  [1.0, 5.0, 9.0, 13.0, 17],  # (1, 4, 5)
                  [2.0, 6.0, 10.0, 14.0, 18],
                  [3.0, 7.0, 11.0, 15.0, 19],
                  [4.0, 8.0, 12.0, 16.0, 20],
              ]
          ]
      ).astype(np.float32)
      image_shape = np.array([6, 6]).astype(np.int64)
      block_shape = np.array([2, 2]).astype(np.int64)
  
      output = np.array(
          [
              [
                  [
                      [1.0, 0.0, 0.0, 0.0, 0.0, 2.0],  # (1, 1, 6, 6)
                      [8.0, 0.0, 0.0, 0.0, 0.0, 10.0],
                      [16.0, 0.0, 0.0, 0.0, 0.0, 18.0],
                      [24.0, 0.0, 0.0, 0.0, 0.0, 26.0],
                      [32.0, 0.0, 0.0, 0.0, 0.0, 34.0],
                      [19.0, 0.0, 0.0, 0.0, 0.0, 20.0],
                  ]
              ]
          ]
      ).astype(np.float32)
  
      node = onnx.helper.make_node(
          "Col2Im",
          ["input", "image_shape", "block_shape"],
          ["output"],
          dilations=[1, 5],
      )
      expect(
          node,
          inputs=[input, image_shape, block_shape],
          outputs=[output],
          name="test_col2im_dilations",
      )
  
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 4 for calling the Col2Im obj's underlying member method code with example usage: obj.export_col2im_dilations() without return types