You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: deberta_v2.rs
    path of file: ./repos/rust-bert/tests
    
    the code of the file until where you have to start completion: use rust_bert::deberta_v2::{
    DebertaV2Config, DebertaV2ConfigResources, DebertaV2ForMaskedLM, DebertaV2ForQuestionAnswering,
    DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2Vo
    
fn deberta_v2_for_sequence_classification() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Box::new(RemoteResource::from_pretrained(
        DebertaV2ConfigResources::DEBERTA_V3_BASE,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        DebertaV2VocabResources::DEBERTA_V3_BASE,
    ));
    let config_path = config_resource.get_local_path()?;
    let vocab_path = vocab_resource.get_local_path()?;

    //    Set-up model
    let device = Device::cuda_if_available();
    let vs = nn::VarStore::new(device);
    let tokenizer =
        DeBERTaV2Tokenizer::from_file(vocab_path.to_str().unwrap(), false, false, false)?;
    let mut config = DebertaV2Config::from_file(config_path);
    let mut dummy_label_mapping = HashMap::new();
    dummy_label_mapping.insert(0, String::from("Positive"));
    dummy_label_mapping.insert(1, String::from("Neutral"));
    dummy_label_mapping.insert(2, String::from("Negative"));
    config.id2label = Some(dummy_label_mapping);
    let model = DebertaV2ForSequenceClassification::new(vs.root(), &config)?;

    //    Define input
    let inputs = ["Where's Paris?", "In Kentucky, United States"];
    let tokenized_input = tokenizer.encode_list(&inputs, 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input
        .iter()
        .map(|input| input.token_ids.len())
        .max()
        .unwrap();
    let tokenized_input = tokenized_input
        .iter()
        .map(|input| input.token_ids.clone())
        .map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        })
        .map(|input| Tensor::from_slice(&(input)))
        .collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

    //    Forward pass
    let model_output =
        no_grad(|| model.forward_t(Some(input_tensor.as_ref()), None, None, None, None, false))?;

    assert_eq!(model_output.logits.size(), &[2, 3]);

    Ok(())
}