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() -> None:
      input = np.array(
          [
              [
                  [1.0, 6.0, 11.0, 16.0, 21.0],  # (1, 5, 5)
                  [2.0, 7.0, 12.0, 17.0, 22.0],
                  [3.0, 8.0, 13.0, 18.0, 23.0],
                  [4.0, 9.0, 14.0, 19.0, 24.0],
                  [5.0, 0.0, 15.0, 20.0, 25.0],
              ]
          ]
      ).astype(np.float32)
  
      image_shape = np.array([5, 5]).astype(np.int64)
      block_shape = np.array([1, 5]).astype(np.int64)
      node = onnx.helper.make_node(
          "Col2Im", ["input", "image_shape", "block_shape"], ["output"]
      )
  
      output = np.array(
          [
              [
                  [
                      [1.0, 2.0, 3.0, 4.0, 5.0],  # (1, 1, 5, 5)
                      [6.0, 7.0, 8.0, 9.0, 0.0],
                      [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],
                  ]
              ]
          ]
      ).astype(np.float32)
  
      expect(
          node,
          inputs=[input, image_shape, block_shape],
          outputs=[output],
          name="test_col2im",
      )
  
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 1 for calling the Col2Im obj's underlying member method code with example usage: obj.export() without return types