def METHOD_NAME(x_size: int, y_size: int, max_slope_percent: float):
    """Itakura parallelogram bounding matrix.
    This code was adapted from tslearn. This link to the orginal code line 974:
    https://github.com/tslearn-team/tslearn/blob/main/tslearn/metrics/dtw_variants.py
    """
    one_percent = min(x_size, y_size) / 100
    max_slope = math.floor(((max_slope_percent * one_percent) * 100))
    min_slope = 1 / float(max_slope)
    max_slope *= float(x_size) / float(y_size)
    min_slope *= float(x_size) / float(y_size)
    lower_bound = np.empty((2, y_size))
    lower_bound[0] = min_slope * np.arange(y_size)
    lower_bound[1] = (
        (x_size - 1) - max_slope * (y_size - 1) + max_slope * np.arange(y_size)
    )
    lower_bound_ = np.empty(y_size)
    for i in range(y_size):
        lower_bound_[i] = max(round(lower_bound[0, i], 2), round(lower_bound[1, i], 2))
    lower_bound_ = np.ceil(lower_bound_)
    upper_bound = np.empty((2, y_size))
    upper_bound[0] = max_slope * np.arange(y_size)
    upper_bound[1] = (
        (x_size - 1) - min_slope * (y_size - 1) + min_slope * np.arange(y_size)
    )
    upper_bound_ = np.empty(y_size)
    for i in range(y_size):
        upper_bound_[i] = min(round(upper_bound[0, i], 2), round(upper_bound[1, i], 2))
    upper_bound_ = np.floor(upper_bound_ + 1)
    bounding_matrix = np.full((x_size, y_size), False)
    for i in range(y_size):
        bounding_matrix[int(lower_bound_[i]) : int(upper_bound_[i]), i] = True
    return bounding_matrix