code: 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
  
imports: import matplotlib.pyplot
  from nilearn import image, plotting
  from nilearn.datasets import fetch_language_localizer_demo_dataset
  from nilearn.glm.first_level import FirstLevelModel, first_level_from_bids
  import numpy
  from nilearn.maskers import NiftiMasker, NiftiSpheresMasker
label_desc: Use image node_id 1 for calling a global function with example usage: lss_transformer(df, row_number) and returns: df,  trial_name