How to use the mercantile.feature function in mercantile

n_neg_ex = int(kwargs['background_ratio'] * len(pos_examples))
        neg_examples = np.random.choice(neg_examples, n_neg_ex, replace=False).tolist()

        tile_results = {k: tile_results.get(k) for k in pos_examples + neg_examples}
        print('Using sparse mode; subselected {} background tiles'.format(n_neg_ex))

    # write out labels as numpy arrays
    labels_file = op.join(dest_folder, 'labels.npz')
    print('Writing out labels to {}'.format(labels_file))
    np.savez(labels_file, **tile_results)

    # write out labels as GeoJSON or PNG
    if ml_type == 'classification':
        features = []
        for tile, label in tile_results.items():
            feat = feature(Tile(*[int(t) for t in tile.split('-')]))
            features.append(Feature(geometry=feat['geometry'],
                                    properties=dict(label=label.tolist())))
        json.dump(fc(features), open(op.join(dest_folder, 'classification.geojson'), 'w'))
    elif ml_type == 'object-detection':
        label_folder = op.join(dest_folder, 'labels')
        if not op.isdir(label_folder):
            makedirs(label_folder)
        for tile, label in tile_results.items():
            # if we have at least one bounding box label
            if bool(label.shape[0]):
                label_file = '{}.png'.format(tile)
                img = Image.new('RGB', (256, 256))
                draw = ImageDraw.Draw(img)
                for box in label:
                    draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=class_color(box[4]))
                print('Writing {}'.format(label_file))

with open(fname_csv, 'r') as csvfile:
        with open(fname_geojson, 'w') as results:
            reader = csv.reader(csvfile)
            first_line = True

            # Create a FeatureCollection
            results.write('{"type":"FeatureCollection","features":[')
            next(reader)  # Skip header

            for row in reader:

                # Load as pygeotile using TMS coords
                geot = tile_func(*[int(t) for t in row[0].split('-')])

                # Create feature with mercantile
                feat = feature(Tile(geot.google[0], geot.google[1], geot.zoom))

                # Get class prediction confidences
                pred = json.loads(','.join(row[1:]))
                pred_red = list(map(lambda x: round(x, 2), pred))
                if pred_red[thresh_ind] >= thresh:
                    # Add commas prior to any feature that isn't the first one
                    if first_line:
                        first_line = False
                    else:
                        results.write(',')

                    pred_obj = dict(zip(map(lambda x: 'p%s' % x,
                                            range(len(pred_red))), pred_red))

                    results.write(json.dumps(Feature(geometry=feat['geometry'],
                                                     properties=pred_obj)))

col_ys = []

    for i, line in enumerate(iter_lines(src)):
        obj = json.loads(line)
        if isinstance(obj, dict):
            x, y, z = obj["tile"][:3]
            props = obj.get("properties")
            fid = obj.get("id")
        elif isinstance(obj, list):
            x, y, z = obj[:3]
            props = {}
            fid = None
        else:
            raise click.BadParameter("{0}".format(obj), param=input, param_hint="input")

        feature = mercantile.feature(
            (x, y, z),
            fid=fid,
            props=props,
            projected=projected,
            buffer=buffer,
            precision=precision,
        )
        bbox = feature["bbox"]
        w, s, e, n = bbox
        col_xs.extend([w, e])
        col_ys.extend([s, n])

        if collect:
            features.append(feature)
        elif extents:
            click.echo(" ".join(map(str, bbox)))