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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
 """ Trains a k-nearest neighbors classifier for face recognition. :param train_dir: directory that contains a sub-directory for each known person, with its name. (View in source code to see train_dir example tree structure) Structure: <train_dir>/ ├── <person1>/ │ ├── <somename1>.jpeg │ ├── <somename2>.jpeg ├── <person2>/ │ ├── <somename1>.jpeg │ └── <somename2>.jpeg :param model_save_path: (optional) path to save model on disk :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree :param verbose: verbosity of training :return: returns knn classifier that was trained on the given data. """

    X = []
    y = []

    # Loop through each person in the training set
    for class_dir in os.listdir(train_dir):
        if not os.path.isdir(os.path.join(train_dir, class_dir)):
            continue

        # Loop through each training image for the current person
        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):
            image = face_recognition.load_image_file(img_path)
            face_bounding_boxes = face_recognition.face_locations(image)

            if len(face_bounding_boxes) != 1:
                # If there are no people (or too many people) in a training image, skip the image.
                if verbose:
                    print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face"))
            else:
                # Add face encoding for current image to the training set
                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])
                y.append(class_dir)

    # Determine how many neighbors to use for weighting in the KNN classifier
    if n_neighbors is None:
        n_neighbors = int(round(math.sqrt(len(X))))
        if verbose:
            print("Chose n_neighbors automatically:", n_neighbors)

    # Create and train the KNN classifier
    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')
    knn_clf.fit(X, y)

    # Save the trained KNN classifier
    if model_save_path is not None:
        with open(model_save_path, 'wb') as f:
            pickle.dump(knn_clf, f)

    return knn_clf