def METHOD_NAME():
    for model_class, tokenizer_class, pretrained_weights, save_name in MODELS:
        # Load pretrained model/tokenizer
        tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
        model = model_class.from_pretrained(pretrained_weights)
        model.eval()
        # Encode text
        # Add special tokens takes care of adding [CLS], [SEP], <s>... tokens in the right way for each model.
        input_ids_1 = torch.tensor(
            [[tokenizer.encode("Here is some text to encode Hello World", add_special_tokens=True)]])
        with torch.no_grad():
            output_1 = model(input_ids_1)  # Models outputs are now tuples
        model_dir, data_dir = save_model(save_name, model.cpu(), input_ids_1, output_1,
                                         opset_version=11,
                                         input_names=['input1'],
                                         dynamic_axes={'input1': [0, 1, 2]})
        # Test exported model with TensorProto data saved in files
        inputs_flatten = flatten(input_ids_1)
        inputs_flatten = update_flatten_list(inputs_flatten, [])
        outputs_flatten = flatten(output_1)
        outputs_flatten = update_flatten_list(outputs_flatten, [])
        inputs = []
        for i, _ in enumerate(inputs_flatten):
            f_ = os.path.join(data_dir, '{0}_{1}.pb'.format("input", i))
            tensor = onnx.TensorProto()
            with open(f_, 'rb') as file:
                tensor.ParseFromString(file.read())
            inputs.append(numpy_helper.to_array(tensor))
        outputs = []
        for i, _ in enumerate(outputs_flatten):
            f_ = os.path.join(data_dir, '{0}_{1}.pb'.format("output", i))
            tensor = onnx.TensorProto()
            with open(f_, 'rb') as file:
                tensor.ParseFromString(file.read())
            outputs.append(numpy_helper.to_array(tensor))
        inference(model_dir, inputs, outputs)
        # Test exported model with a new input
        print("== Feeding model with new input ==")
        input_ids_2 = torch.tensor(
              [[tokenizer.encode("Here is some alternative text to encode I love Seattle", add_special_tokens=True)]])
        with torch.no_grad():
            output_2 = model(input_ids_2)
        inference(model_dir, input_ids_2, output_2)