input: Write a python function to calculate the mean and standard deviation of a specified column, and return the unique values of another specified column in a given DataFrame.
  assert analyze_columns(pd.DataFrame({'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'A', 'B', 'C']}), 'col1', 'col2') == (3.0, 1.5811388300841898, {'A', 'B', 'C'})
  assert analyze_columns(pd.DataFrame({'col1': [10, 20, 30, 40, 50], 'col2': ['X', 'Y', 'X', 'Y', 'Z']}), 'col1', 'col2') == (30.0, 15.811388300841896, {'X', 'Y', 'Z'})
  assert analyze_columns(pd.DataFrame({'col1': [5, 10, 15, 20, 25], 'col2': ['M', 'N', 'O', 'P', 'Q']}), 'col1', 'col2') == (15.0, 7.905694150420948, {'M', 'N', 'O', 'P', 'Q'})
output: def analyze_columns(df, col1, col2):
      mean_val = df[col1].mean()
      std_dev = df[col1].std()
      unique_vals = set(df[col2])
      return (mean_val, std_dev, unique_vals)