def stack_seeds(n_row: int, n_col: int, seeds_row: Optional[Union[(np.ndarray, dict)]], seeds_col: Optional[Union[(np.ndarray, dict)]]=None, default_value: float=(- 1)) -> np.ndarray:
    'Process seeds for rows and columns and stack the results into a single vector.'
    if ((seeds_row is None) and (seeds_col is None)):
        seeds_row = np.ones(n_row)
        seeds_col = (default_value * np.ones(n_col))
    elif (seeds_row is None):
        seeds_row = (default_value * np.ones(n_row))
    elif (seeds_col is None):
        seeds_col = (default_value * np.ones(n_col))
    seeds_row = check_seeds(seeds_row, n_row)
    seeds_col = check_seeds(seeds_col, n_col)
    return np.hstack((seeds_row, seeds_col))
Process seeds for rows and columns and stack the results into a single vector.
def stack_seeds(n_row: int, n_col: int, seeds_row: Optional[Union[(np.ndarray, dict)]], seeds_col: Optional[Union[(np.ndarray, dict)]]=None, default_value: float=(- 1)) -> np.ndarray:
    
    if ((seeds_row is None) and (seeds_col is None)):
        seeds_row = np.ones(n_row)
        seeds_col = (default_value * np.ones(n_col))
    elif (seeds_row is None):
        seeds_row = (default_value * np.ones(n_row))
    elif (seeds_col is None):
        seeds_col = (default_value * np.ones(n_col))
    seeds_row = check_seeds(seeds_row, n_row)
    seeds_col = check_seeds(seeds_col, n_col)
    return np.hstack((seeds_row, seeds_col))