code: def convert_Repeat(
      func, opset_version, input_names, output_names, context
  ):
      repeats = func.repeats
      if len(repeats) > 1:
          raise NotImplementedError(
              "ONNX-Chainer currently does not support elementwise repeat"
          )
  
      gb = onnx_helper.GraphBuilder()
      inputs = list(input_names)
      axis = func.axis
      if axis is None:
          shape_name = context.add_const(
              np.array([-1], dtype=np.int64), "shape"
          )
          input_names.append(shape_name)
          inputs = [gb.op("Reshape", input_names)]
          scales = [float(repeats[0])]
      else:
          scales = [1.0] * func.inputs[0].data.ndim
          scales[axis] = float(repeats[0])
  
      if opset_version == 7:
          gb.op_output_named(
              "Upsample", inputs, output_names, scales=scales
          )
          return gb.nodes()
  
      scales_name = context.add_const(
          np.array(scales, dtype=np.float32), "scales"
      )
  
      if opset_version in [9, 10]:
          inputs.append(scales_name)
          op = "Upsample" if opset_version == 9 else "Resize"
          gb.op_output_named(op, inputs, output_names)
          return gb.nodes()
  
      if opset_version == 11:
          roi = context.add_const(np.array([]), "roi")
          inputs.extend([roi, scales_name])
          gb.op_output_named("Resize", inputs, output_names)
          return gb.nodes()
  
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 20 for calling a global function with example usage: convert_Repeat(func, opset_version, input_names, output_names, context) and returns: gb, gb, gb