docs: def load_robot_execution_failures(multiclass=False):
      """
      Load the Robot Execution Failures LP1 Data Set[1].
      The Time series are passed as a flat DataFrame.
  
      Examples
      ========
  
      >>> from tsfresh.examples import load_robot_execution_failures
      >>> df, y = load_robot_execution_failures()
      >>> print(df.shape)
      (1320, 8)
  
      :param multiclass: If True, return all target labels. The default returns only "normal" vs all other labels.
      :type multiclass: bool
      :return: time series data as :class:`pandas.DataFrame` and target vector as :class:`pandas.Series`
      :rtype: tuple
      """
      if not os.path.exists(data_file_name):
          raise RuntimeError(UCI_MLD_REF_MSG)
  
      id_to_target = {}
      df_rows = []
  
      with open(data_file_name) as f:
          cur_id = 0
          time = 0
  
          for line in f.readlines():
              # New sample --> increase id, reset time and determine target
              if line[0] not in ['\t', '\n']:
                  cur_id += 1
                  time = 0
                  if multiclass:
                      id_to_target[cur_id] = line.strip()
                  else:
                      id_to_target[cur_id] = (line.strip() == 'normal')
              # Data row --> split and convert values, create complete df row
              elif line[0] == '\t':
                  values = list(map(int, line.split('\t')[1:]))
                  df_rows.append([cur_id, time] + values)
                  time += 1
  
      df = pd.DataFrame(df_rows, columns=['id', 'time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z'])
      y = pd.Series(id_to_target)
  
      return df, y