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: natural_language_inference_deberta.rs
    path of file: ./repos/rust-bert/examples
    
    the code of the file until where you have to start completion: extern crate anyhow;

use rust_bert::deberta::{
    DebertaConfig, DebertaConfigResources, DebertaForSequenceClassification,
    DebertaMergesResources, DebertaModelResources, DebertaVocabResources,
};
use rust_bert::resources::{load_weights, RemoteResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::{DeBERTaTokenizer, MultiThreadedTokenizer, TruncationStrategy};
use tch::{nn, no_grad, Device, Kind, Tensor};

fn main() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Box::new(RemoteResource::from_pretrained(
        DebertaConfigResources::DEBERTA_BASE_MNLI,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        DebertaVocabResources::DEBERTA_BASE_MNLI,
    ));
    let merges_resource = Box::new(RemoteResource::from_pretrained(
        DebertaMergesResources::DEBERTA_BASE_MNLI,
    ))
    
fn main() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Box::new(RemoteResource::from_pretrained(
        DebertaConfigResources::DEBERTA_BASE_MNLI,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        DebertaVocabResources::DEBERTA_BASE_MNLI,
    ));
    let merges_resource = Box::new(RemoteResource::from_pretrained(
        DebertaMergesResources::DEBERTA_BASE_MNLI,
    ));
    let model_resource = Box::new(RemoteResource::from_pretrained(
        DebertaModelResources::DEBERTA_BASE_MNLI,
    ));

    let config_path = config_resource.get_local_path()?;
    let vocab_path = vocab_resource.get_local_path()?;
    let merges_path = merges_resource.get_local_path()?;

    //    Set-up model
    let device = Device::Cpu;
    let mut vs = nn::VarStore::new(device);
    let tokenizer = DeBERTaTokenizer::from_file(
        vocab_path.to_str().unwrap(),
        merges_path.to_str().unwrap(),
        false,
    )?;
    let config = DebertaConfig::from_file(config_path);
    let model = DebertaForSequenceClassification::new(vs.root(), &config)?;
    load_weights(&model_resource, &mut vs, None, device)?;

    //    Define input
    let input = [("I love you.", "I like you.")];

    let tokenized_input = MultiThreadedTokenizer::encode_pair_list(
        &tokenizer,
        &input,
        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), None, None, None, None, false))?;

    model_output.logits.softmax(-1, Kind::Float).print();

    Ok(())
}