code: def main():
      args = parser.parse_args()
  
      args.pretrained = True
      if args.checkpoint:
          args.pretrained = False
  
      print("==> Creating PyTorch {} model".format(args.model))
      # NOTE exportable=True flag disables autofn/jit scripted activations and uses Conv2dSameExport layers
      # for models using SAME padding
      model = timm.create_model(
          args.model,
          num_classes=args.num_classes,
          in_chans=3,
          pretrained=args.pretrained,
          checkpoint_path=args.checkpoint,
          exportable=True,
      )
  
      if args.reparam:
          model = reparameterize_model(model)
  
      onnx_export(
          model,
          args.output,
          opset=args.opset,
          dynamic_size=args.dynamic_size,
          aten_fallback=args.aten_fallback,
          keep_initializers=args.keep_init,
          check_forward=args.check_forward,
          training=args.training,
          verbose=args.verbose,
          input_size=(3, args.img_size, args.img_size),
          batch_size=args.batch_size,
      )
  
imports: import argparse
  import timm
  from timm.utils.model import reparameterize_model
  from timm.utils.onnx import onnx_export
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types