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: base_nb.rs
    path of file: ./repos/linfa/algorithms/linfa-bayes/src
    
    the code of the file until where you have to start completion: use ndarray::{s, Array1, Array2, ArrayBase, ArrayView1, ArrayView2, Axis, Data, Ix2};
use ndarray_stats::QuantileExt;
use std::collections::HashMap;

use crate::error::{NaiveBayesError, Result};
use linfa::dataset::{AsTargets, DatasetBase, Labels};
use linfa::traits::FitWith;
use linfa::{Float, Label};

// Trait computing predictions for fitted Naive Bayes models
pub(crate) trait NaiveBayes<'a, F, L>
where
    F: Float,
    L: Label + Ord,
{
    fn joint_log_likelihood(&self, x: ArrayView2<F>) -> HashMap<&L, Array1<F>>;

    fn predict_inplace<D: Data<Elem = F>>(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<L>) {
        assert_
    
    fn predict_inplace<D: Data<Elem = F>>(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<L>) {
        assert_eq!(
            x.nrows(),
            y.len(),
            "The number of data points must match the number of output targets."
        );

        let joint_log_likelihood = self.joint_log_likelihood(x.view());

        // We store the classes and likelihood info in an vec and matrix
        // respectively for easier identification of the dominant class for
        // each input
        let nclasses = joint_log_likelihood.keys().len();
        let n = x.nrows();
        let mut classes = Vec::with_capacity(nclasses);
        let mut likelihood = Array2::zeros((nclasses, n));
        joint_log_likelihood
            .iter()
            .enumerate()
            .for_each(|(i, (&key, value))| {
                classes.push(key.clone());
                likelihood.row_mut(i).assign(value);
            });

        // Identify the class with the maximum log likelihood
        *y = likelihood.map_axis(Axis(0), |x| {
            let i = x.argmax().unwrap();
            classes[i].clone()
        });
    }