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: norm_scaler_bench.rs
    path of file: ./repos/linfa/algorithms/linfa-preprocessing/benches
    
    the code of the file until where you have to start completion: use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
use linfa::benchmarks::config;
use linfa::traits::Transformer;
use linfa_datasets::generate::make_dataset;
use linfa_preprocessing::norm_scaling::NormScaler;
use statrs::distribution::{DiscreteUniform, Laplace};

fn bench(c: &mut Criterion) {
    let mut benchmark = c.benchmark_group("norm scaler");
    config::set_default_benchmark_configs(&mut benchmark);
    let size = 10000;
    let feat_distr = Laplace::new(0.5, 5.).unwrap();
    let target_distr = DiscreteUniform::new(0, 5).unwrap();

    for (scaler, fn_name) in [
        (NormScaler::l2(), "l2 scaler"),
        (NormScaler::l1(), "l1 scaler"),
        (NormScaler::max(), "max scaler"),
    ] {
        for nfeatures in (10..100).step_by(10) {
            let dataset = make_dataset(size, nfeatures, 1, feat_distr, target_dist
    
fn bench(c: &mut Criterion) {
    let mut benchmark = c.benchmark_group("norm scaler");
    config::set_default_benchmark_configs(&mut benchmark);
    let size = 10000;
    let feat_distr = Laplace::new(0.5, 5.).unwrap();
    let target_distr = DiscreteUniform::new(0, 5).unwrap();

    for (scaler, fn_name) in [
        (NormScaler::l2(), "l2 scaler"),
        (NormScaler::l1(), "l1 scaler"),
        (NormScaler::max(), "max scaler"),
    ] {
        for nfeatures in (10..100).step_by(10) {
            let dataset = make_dataset(size, nfeatures, 1, feat_distr, target_distr);
            benchmark.bench_function(
                BenchmarkId::new(fn_name, format!("{}x{}", nfeatures, size)),
                |bencher| {
                    bencher.iter(|| {
                        scaler.transform(black_box(dataset.view()));
                    });
                },
            );
        }
    }
}