code: def normal_quantiles_df(
      df,
      std_col,
      mean_col=None,
      fixed_mean=0.0,
      quantiles=(0.025, 0.975),
      quantile_summary_col="normal_quantiles",
  ):
      """Calculates normal quantiles for each row of ``df``
      using available means (either given in ``mean_col`` or ``fixed_mean``) and
      standard deviations (given in ``std_col``).
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          A DataFrame containing the standard deviations.
      fixed_mean : `float`, default 0
          Fixed sample mean to add to each row of ``df``.
          It is only used if ``mean_col`` is None.
      mean_col : `str` or None, default None
          The column with the sample means for each row of ``df``.
      std_col : `str`
          The column with the sample std for each row
      quantiles : `list` [`float`]
          The normal quantiles to be calculated for each row of ``df``.
      quantile_summary_col : `str`, default "normal_quantiles"
          The column name where the computed quantiles are stored.
  
      Returns
      -------
      df : `pandas.DataFrame`
          ``df`` augmented with normal quantiles for each row.
      """
      df = df.copy()
      if mean_col is None:
          df[quantile_summary_col] = df.apply(
              lambda row: stats.norm.ppf(
                  loc=fixed_mean, scale=row[std_col], q=quantiles
              ),
              axis=1,
          )
      else:
          df[quantile_summary_col] = df.apply(
              lambda row: stats.norm.ppf(
                  loc=row[mean_col], scale=row[std_col], q=quantiles
              ),
              axis=1,
          )
  
      return df
  
label_desc: Use image node_id 1 for calling a global function with example usage: normal_quantiles_df(df, std_col, mean_col, fixed_mean, quantiles, quantile_summary_col) and returns: df