class_docstr: Basic class of models
  
  Attributes:
      hparams (HParams): A HParams object, holds the entire set of hyperparameters.
      train_iterator (object): An iterator to load the data in training steps.
      test_iterator (object): An iterator to load the data in testing steps.
      graph (object): An optional graph.
      seed (int): Random seed.
class_docstr_tok: ["Basic","class","of","models","Attributes",":","hparams","(","HParams",")",":","A","HParams","object",",","holds","the","entire","set","of","hyperparameters",".","train_iterator","(","object",")",":","An","iterator","to","load","the","data","in","training","steps",".","test_iterator","(","object",")",":","An","iterator","to","load","the","data","in","testing","steps",".","graph","(","object",")",":","An","optional","graph",".","seed","(","int",")",":","Random","seed","."]
code: def run_fast_eval(self, news_filename, behaviors_file):
      news_vecs = self.run_news(news_filename)
      user_vecs = self.run_user(news_filename, behaviors_file)
  
      self.news_vecs = news_vecs
      self.user_vecs = user_vecs
  
      group_impr_indexes = []
      group_labels = []
      group_preds = []
  
      for (
          impr_index,
          news_index,
          user_index,
          label,
      ) in tqdm(
          self.test_iterator.load_impression_from_file(behaviors_file)
      ):
          pred = np.dot(
              np.stack([news_vecs[i] for i in news_index], axis=0),
              user_vecs[impr_index],
          )
          group_impr_indexes.append(impr_index)
          group_labels.append(label)
          group_preds.append(pred)
  
      return group_impr_indexes, group_labels, group_preds
  
imports: import abc
  import time
  import numpy
  from tqdm import tqdm
  import tensorflow
  from tensorflow.compat.v1 import keras
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 18 for calling the BaseModel obj's underlying member method code with example usage: obj.run_fast_eval(news_filename, behaviors_file) and returns: group_impr_indexes,  group_labels,  group_preds