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_strides() -> None:
      input = np.array(
          [
              [
                  [0.0, 0.0, 0.0, 0.0],  # (1, 9, 4)
                  [1.0, 1.0, 1.0, 1.0],
                  [1.0, 1.0, 1.0, 1.0],
                  [1.0, 1.0, 1.0, 1.0],
                  [0.0, 0.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 0.0],
                  [1.0, 1.0, 1.0, 1.0],
                  [0.0, 0.0, 0.0, 0.0],
              ]
          ]
      ).astype(np.float32)
      image_shape = np.array([5, 5]).astype(np.int64)
      block_shape = np.array([3, 3]).astype(np.int64)
  
      output = np.array(
          [
              [
                  [
                      [0.0, 1.0, 1.0, 1.0, 1.0],  # (1, 1, 5, 5)
                      [1.0, 0.0, 1.0, 0.0, 0.0],
                      [0.0, 2.0, 1.0, 2.0, 1.0],
                      [1.0, 0.0, 1.0, 0.0, 0.0],
                      [0.0, 1.0, 0.0, 1.0, 0.0],
                  ]
              ]
          ]
      ).astype(np.float32)
  
      node = onnx.helper.make_node(
          "Col2Im",
          ["input", "image_shape", "block_shape"],
          ["output"],
          strides=[2, 2],
      )
      expect(
          node,
          inputs=[input, image_shape, block_shape],
          outputs=[output],
          name="test_col2im_strides",
      )
  
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 2 for calling the Col2Im obj's underlying member method code with example usage: obj.export_col2im_strides() without return types