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/burn/examples/pytorch-import/src
    
    the code of the file until where you have to start completion: use std::env::args;
use std::path::Path;

use burn::{
    backend::ndarray::NdArray,
    data::dataset::{vision::MnistDataset, Dataset},
    record::{FullPrecisionSettings, NamedMpkFileRecorder, Recorder},
    tensor::Tensor,
};

use model::Model;

const IMAGE_INX: usize = 42; // <- Change this to test a different image

// Build output direct that contains converted model weight fi
    
fn main() {
    // Get image index argument (first) from command line

    let image_index = if let Some(image_index) = args().nth(1) {
        println!("Image index: {}", image_index);
        image_index
            .parse::<usize>()
            .expect("Failed to parse image index")
    } else {
        println!("No image index provided; Using default image index: {IMAGE_INX}");
        IMAGE_INX
    };

    assert!(image_index < 10000, "Image index must be less than 10000");

    type Backend = NdArray<f32>;
    let device = Default::default();

    // Load the model record from converted PyTorch file by the build script
    let record = NamedMpkFileRecorder::<FullPrecisionSettings>::default()
        .load(Path::new(OUT_DIR).into(), &device)
        .expect("Failed to decode state");

    // Create a new model and load the state
    let model: Model<Backend> = Model::new_with(record);

    // Load the MNIST dataset and get an item
    let dataset = MnistDataset::test();
    let item = dataset.get(image_index).unwrap();

    // Create a tensor from the image data
    let image_data = item.image.iter().copied().flatten().collect::<Vec<f32>>();
    let mut input: Tensor<Backend, 4> =
        Tensor::from_floats(image_data.as_slice(), &device).reshape([1, 1, 28, 28]);

    // Normalize the input
    input = ((input / 255) - 0.1307) / 0.3081;

    // Run the model on the input
    let output = model.forward(input);

    // Get the index of the maximum value
    let arg_max = output.argmax(1).into_scalar() as u8;

    // Check if the index matches the label
    assert!(arg_max == item.label);

    println!("Success!");
    println!("Predicted: {}", arg_max);
    println!("Actual: {}", item.label);

    // Print the image URL if the image index is less than 100 (the online dataset only has 100 images)
    if image_index < 100 {
        println!("See the image online, click the link below:");
        println!("https://datasets-server.huggingface.co/assets/mnist/--/mnist/test/{image_index}/image/image.jpg");
    }
}