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: main.rs
    path of file: ./repos/candle/candle-examples/examples/distilbert
    
    the code of the file until where you have to start completion: #[cfg(feature = "mkl")]
extern crate intel_mkl_src;

#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};

use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;

#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// Run on CPU rather than on GPU.
    #[arg(long)]
    cpu: bool,

    /// Enable tracing (generates a trace-timestamp.json file).
    #[arg(long)]
    tracing: bool,

    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
    #[arg(long)]
    model_id: Option<String>,

    #[arg(long)]
    revision: Option<String>,

    /// When set, compute embeddings for this prompt.
    
fn main() -> Result<()> {
    use tracing_chrome::ChromeLayerBuilder;
    use tracing_subscriber::prelude::*;

    let args = Args::parse();
    let _guard = if args.tracing {
        println!("tracing...");
        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
        tracing_subscriber::registry().with(chrome_layer).init();
        Some(guard)
    } else {
        None
    };
    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
    let device = &model.device;

    let tokenizer = tokenizer
        .with_padding(None)
        .with_truncation(None)
        .map_err(E::msg)?;
    let tokens = tokenizer
        .encode(args.prompt, true)
        .map_err(E::msg)?
        .get_ids()
        .to_vec();
    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
    let mask = get_mask(tokens.len(), device);

    println!("token_ids: {:?}", token_ids.to_vec2::<u32>());
    println!("mask: {:?}", mask.to_vec2::<u8>());

    let ys = model.forward(&token_ids, &mask)?;
    println!("{ys}");

    Ok(())
}