def METHOD_NAME(args, model):
    with torch.no_grad():
        symbolic_names = {0: 'batch_size', 1: 'max_seq_len'}
        if args.model_name_or_path in ['Intel/roberta-base-mrpc', 
                                        'Intel/xlm-roberta-base-mrpc', 
                                        'Intel/camembert-base-mrpc', 
                                        'distilbert-base-uncased-finetuned-sst-2-english',
                                        'Intel/xlnet-base-cased-mrpc',
                                        'Intel/deberta-v3-base-mrpc']:
            inputs = {'input_ids':      torch.ones(1, args.max_len, dtype=torch.int64),
                      'attention_mask': torch.ones(1, args.max_len, dtype=torch.int64)}
            torch.onnx.export(model,                            # model being run
                            (inputs['input_ids'],               # model input (or a tuple for multiple inputs) 
                            inputs['attention_mask']),          
                            args.output_model,                  # where to save the model (can be a file or file-like object)
                            opset_version=14,                   # the ONNX version to export the model
                            do_constant_folding=True,           # whether to execute constant folding
                            input_names=['input_ids',           # the model's input names
                                        'attention_mask'],
                            output_names=['logits'],
                            dynamic_axes={'input_ids': symbolic_names,        # variable length axes
                                        'attention_mask' : symbolic_names})
        else:
            inputs = {'input_ids':      torch.ones(1, args.max_len, dtype=torch.int64),
                      'attention_mask': torch.ones(1, args.max_len, dtype=torch.int64),
                      'token_type_ids': torch.ones(1, args.max_len, dtype=torch.int64)}
            torch.onnx.export(model,                            # model being run
                            (inputs['input_ids'],               # model input (or a tuple for multiple inputs) 
                            inputs['attention_mask'],
                            inputs['token_type_ids']),          
                            args.output_model,                  # where to save the model (can be a file or file-like object)
                            opset_version=14,                   # the ONNX version to export the model
                            do_constant_folding=True,           # whether to execute constant folding
                            input_names=['input_ids',           # the model's input names
                                         'attention_mask',
                                         'token_type_ids'],
                            output_names=['logits'],
                            dynamic_axes={'input_ids': symbolic_names,        # variable length axes
                                          'attention_mask' : symbolic_names,
                                          'token_type_ids' : symbolic_names})
        print("ONNX Model exported to {0}".format(args.output_model))