class_docstr: Data loader for FFM format based models, such as xDeepFM.
  Iterator will not load the whole data into memory. Instead, it loads data into memory
  per mini-batch, so that large files can be used as input data.
docstr: Parse one string line into feature values.
  
  Args:
      line (str): A string indicating one instance.
  
  Returns:
      list: Parsed results, including `label`, `features` and `impression_id`.
docstr_tok: ["Parse","one","string","line","into","feature","values",".","Args",":","line","(","str",")",":","A","string","indicating","one","instance",".","Returns",":","list",":","Parsed","results",",","including","`","label","`",",","`","features","`","and","`","impression_id","`","."]
code: def parser_one_line(self, line):
      """Parse one string line into feature values.
  
      Args:
          line (str): A string indicating one instance.
  
      Returns:
          list: Parsed results, including `label`, `features` and `impression_id`.
  
      """
      impression_id = 0
      words = line.strip().split(self.ID_spliter)
      if len(words) == 2:
          impression_id = words[1].strip()
  
      cols = words[0].strip().split(self.col_spliter)
  
      label = float(cols[0])
  
      features = []
      for word in cols[1:]:
          if not word.strip():
              continue
          tokens = word.split(":")
          features.append(
              [int(tokens[0]) - 1, int(tokens[1]) - 1, float(tokens[2])]
          )
  
      return label, features, impression_id
  
file_path: recommenders/recommenders/models/deeprec/io/iterator.py
imports: import numpy
  import tensorflow
  import abc
label_desc: Use image node_id 2 for calling the FFMTextIterator obj's underlying member method code with example usage: obj.parser_one_line(line) and returns: label,  features,  impression_id