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/tract/examples/tensorflow-mobilenet-v2/src
    
    the code of the file until where you have to start completion: use tract_tensorflow::prelude::*;

fn main() -> TractResult<()> {
    let model = tract_tensorflow::tensorflow()
        // load the model
        .model_for_path("mobilenet_v2_1.4_224_frozen.pb")?
        // specify input type and shape
        .with_input_fact(0, f32::fact([1, 224, 224, 3]).into())?
        // optimize the model
        .into_optimized()?
        // make the model runnable and fix its inputs and o
    
fn main() -> TractResult<()> {
    let model = tract_tensorflow::tensorflow()
        // load the model
        .model_for_path("mobilenet_v2_1.4_224_frozen.pb")?
        // specify input type and shape
        .with_input_fact(0, f32::fact([1, 224, 224, 3]).into())?
        // optimize the model
        .into_optimized()?
        // make the model runnable and fix its inputs and outputs
        .into_runnable()?;

    // open image, resize it and make a Tensor out of it
    let image = image::open("grace_hopper.jpg").unwrap().to_rgb8();
    let resized =
        image::imageops::resize(&image, 224, 224, ::image::imageops::FilterType::Triangle);
    let image: Tensor = tract_ndarray::Array4::from_shape_fn((1, 224, 224, 3), |(_, y, x, c)| {
        resized[(x as _, y as _)][c] as f32 / 255.0
    })
    .into();

    // run the model on the input
    let result = model.run(tvec!(image.into()))?;

    // find and display the max value with its index
    let best = result[0]
        .to_array_view::<f32>()?
        .iter()
        .cloned()
        .zip(1..)
        .max_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
    println!("result: {best:?}");
    Ok(())
}