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: translation_m2m100.rs
    path of file: ./repos/rust-bert/examples
    
    the code of the file until where you have to start completion: // Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
// Copyright 2019 Guillaume Becquin
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//     http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

extern crate anyhow;

use rust_bert::m2m_100::{
    M2M100ConfigResources, M2M100MergesResources, M2M100ModelResources, M2M100SourceLanguages,
    M2M100TargetLanguages, M2M100VocabResources,
};
use rust_bert::pipelines::common::{ModelResource, ModelType};
use rust_bert::pipelines::translation::{Language, TranslationConfig, TranslationModel};
use rust_bert::resources::RemoteResource;
use tch::Device;

fn main() -> anyhow::Result<()> {
    let model_resource = RemoteResource::from_pretrained(M2M100ModelResources::M2M100_418M);
    let config_resource = RemoteResource::from_pretrained(M2M100ConfigResources::M2M100_418M);
    let vocab_
    
fn main() -> anyhow::Result<()> {
    let model_resource = RemoteResource::from_pretrained(M2M100ModelResources::M2M100_418M);
    let config_resource = RemoteResource::from_pretrained(M2M100ConfigResources::M2M100_418M);
    let vocab_resource = RemoteResource::from_pretrained(M2M100VocabResources::M2M100_418M);
    let merges_resource = RemoteResource::from_pretrained(M2M100MergesResources::M2M100_418M);

    let source_languages = M2M100SourceLanguages::M2M100_418M;
    let target_languages = M2M100TargetLanguages::M2M100_418M;

    let translation_config = TranslationConfig::new(
        ModelType::M2M100,
        ModelResource::Torch(Box::new(model_resource)),
        config_resource,
        vocab_resource,
        Some(merges_resource),
        source_languages,
        target_languages,
        Device::cuda_if_available(),
    );
    let model = TranslationModel::new(translation_config)?;

    let source_sentence = "This sentence will be translated in multiple languages.";

    let mut outputs = Vec::new();
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::French)?);
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::Spanish)?);
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::Hindi)?);

    for sentence in outputs {
        println!("{sentence}");
    }
    Ok(())
}