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: iris.rs
    path of file: ./repos/modelfox/crates/tree/benchmarks
    
    the code of the file until where you have to start completion: use modelfox_table::prelude::*;
use modelfox_tree::Progress;
use ndarray::prelude::*;
use serde_json::json;
use std::path::Path;

fn main() {
	// Load the data.
	let csv_file_path_train = Path::new("data/iris_train.csv");
	let csv_file_path_test = Path::new("data/iris_test.csv");
	let _n_rows_train = 120;
	let n_rows_test = 30;
	let target_column_index = 4;
    
fn main() {
	// Load the data.
	let csv_file_path_train = Path::new("data/iris_train.csv");
	let csv_file_path_test = Path::new("data/iris_test.csv");
	let _n_rows_train = 120;
	let n_rows_test = 30;
	let target_column_index = 4;
	let n_classes = 3;
	let mut features_train =
		Table::from_path(csv_file_path_train, Default::default(), &mut |_| {}).unwrap();
	let labels_train = features_train.columns_mut().remove(target_column_index);
	let mut features_test =
		Table::from_path(csv_file_path_test, Default::default(), &mut |_| {}).unwrap();
	let labels_test = features_test.columns_mut().remove(target_column_index);
	let labels_train = labels_train.as_enum().unwrap();
	let labels_test = labels_test.as_enum().unwrap();

	// Train the model.
	let train_output = modelfox_tree::MulticlassClassifier::train(
		features_train.view(),
		labels_train.view(),
		&Default::default(),
		Progress {
			kill_chip: &modelfox_kill_chip::KillChip::default(),
			handle_progress_event: &mut |_| {},
		},
	);

	// Make predictions on the test data.
	let mut probabilities = Array::zeros((n_rows_test, 3));
	let features_test = features_test.to_rows();
	train_output
		.model
		.predict(features_test.view(), probabilities.view_mut());

	// Compute Metrics.
	let mut metrics = modelfox_metrics::MulticlassClassificationMetrics::new(n_classes);
	metrics.update(modelfox_metrics::MulticlassClassificationMetricsInput {
		probabilities: probabilities.view(),
		labels: labels_test.view().as_slice().into(),
	});
	let metrics = metrics.finalize();

	let output = json!({
		"accuracy": metrics.accuracy,
	});
	println!("{}", output);
}