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: Construct a dictionary that maps graph elements to values.
  
  Args:
      data_dict (dict): A dictionary that maps string name to numpy arrays.
  
  Returns:
      dict: A dictionary that maps graph elements to numpy arrays.
docstr_tok: ["Construct","a","dictionary","that","maps","graph","elements","to","values",".","Args",":","data_dict","(","dict",")",":","A","dictionary","that","maps","string","name","to","numpy","arrays",".","Returns",":","dict",":","A","dictionary","that","maps","graph","elements","to","numpy","arrays","."]
code: def gen_feed_dict(self, data_dict):
      """Construct a dictionary that maps graph elements to values.
  
      Args:
          data_dict (dict): A dictionary that maps string name to numpy arrays.
  
      Returns:
          dict: A dictionary that maps graph elements to numpy arrays.
  
      """
      feed_dict = {
          self.labels: data_dict["labels"],
          self.fm_feat_indices: data_dict["fm_feat_indices"],
          self.fm_feat_values: data_dict["fm_feat_values"],
          self.fm_feat_shape: data_dict["fm_feat_shape"],
          self.dnn_feat_indices: data_dict["dnn_feat_indices"],
          self.dnn_feat_values: data_dict["dnn_feat_values"],
          self.dnn_feat_weights: data_dict["dnn_feat_weights"],
          self.dnn_feat_shape: data_dict["dnn_feat_shape"],
      }
      return feed_dict
  
file_path: recommenders/recommenders/models/deeprec/io/iterator.py
imports: import numpy
  import tensorflow
  import abc
label_desc: Use image node_id 5 for calling the FFMTextIterator obj's underlying member method code with example usage: obj.gen_feed_dict(data_dict) and returns: feed_dict