You are free to use the implementation in the notes (../../Lectures/03/e_distance_metrics.ipynb), but please make sure you actually cite it appropriately if you copy it verbatim!2. Implement the function knn_euclidean(k, v, d)that takes two arguments, the number of neighbors to return k, the data vector v and the entire dataset d. The algorithm will do the following:1. compute the distance between and all vectors in (with removed, if you like, but the distance between and itself will be ... 0!). You will use the function from part 1 d_euclidean. 2. sort the distances of all vectors in ascending order with the closest (lowest) distances first. 3. return the k top neighbors