code: def convert_ResizeImages(
      func, opset_version, input_names, output_names, context
  ):
      warnings.warn(
          "`resize_images` is mapped to `Upsampling` ONNX op with bilinear "
          "interpolation. "
          "Behavior of bilinear interpolation differs from each implementation. "
          "See the issue https://github.com/chainer/onnx-chainer/issues/147 "
          "for details.",
          UserWarning,
      )
  
      outsize = (func.out_H, func.out_W)
  
      h, w = func.inputs[0].shape[2:]
  
      # Compute scaling factor.
      # NOTE(syoyo): Despite of its name, `Upsample` onnx op will downsample
      # images when scale value is less than 1.0
      scales = [
          1.0,
          1.0,
          float(outsize[0]) / float(h),
          float(outsize[1]) / float(w),
      ]
  
      if (scales[2] < 1.0e-8) and (scales[3] < 1.0e-8):
          raise ValueError(
              "scaling factor is too small or zero. scales for h = {}, scales "
              "for w = {}".format(scales[2], scales[3])
          )
  
      # resize_images in Chainer only supports bilinear interpolation
      # Actually this will be mapped to 'bilinear' in onnxruntime
      mode = "linear"
      if opset_version == 7:
          return (
              onnx_helper.make_node(
                  "Upsample",
                  input_names,
                  output_names,
                  scales=scales,
                  mode=mode,
              ),
          )
  
      scales_name = context.add_const(
          np.array(scales, dtype=np.float32), "scales"
      )
      if opset_version in [9, 10]:
          input_names.append(scales_name)
          op = "Upsample" if opset_version == 9 else "Resize"
          return (
              onnx_helper.make_node(
                  op, input_names, output_names, mode=mode
              ),
          )
  
      if opset_version == 11:
          roi_name = context.add_const(np.array([]), "roi")
          input_names.extend([roi_name, scales_name])
          return (
              onnx_helper.make_node(
                  "Resize", input_names, output_names, mode=mode
              ),
          )
  
imports: import warnings
  import chainer
  import numpy
  import onnx
  from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 21 for calling a global function with example usage: convert_ResizeImages(func, opset_version, input_names, output_names, context) and returns: onnx_helper, onnx_helper, onnx_helper