def lss_transformer(df, row_number):
    """Label one trial for one LSS model.

    Parameters
    ----------
    df : pandas.DataFrame
        BIDS-compliant events file information.
    row_number : int
        Row number in the DataFrame.
        This indexes the trial that will be isolated.

    Returns
    -------
    df : pandas.DataFrame
        Update events information, with the select trial's trial type isolated.
    trial_name : str
        Name of the isolated trial's trial type.
    """
    df = df.copy()

    # Determine which number trial it is *within the condition*
    trial_condition = df.loc[row_number, 'trial_type']
    trial_type_series = df['trial_type']
    trial_type_series = trial_type_series.loc[
        trial_type_series == trial_condition
    ]
    trial_type_list = trial_type_series.index.tolist()
    trial_number = trial_type_list.index(row_number)

    # We use a unique delimiter here (``__``) that shouldn't be in the
    # original condition names.
    # Technically, all you need is for the requested trial to have a unique
    # 'trial_type' *within* the dataframe, rather than across models.
    # However, we may want to have meaningful 'trial_type's (e.g., 'Left_001')
    # across models, so that you could track individual trials across models.
    trial_name = f'{trial_condition}__{trial_number:03d}'
    df.loc[row_number, 'trial_type'] = trial_name
    return df, trial_name