class_docstr: Base class for models
class_docstr_tok: ["Base","class","for","models"]
docstr: Evaluate the data in `feed_dict` with current model.
  
  Args:
      sess (object): The model session object.
      feed_dict (dict): Feed values for evaluation. This is a dictionary that maps graph elements to values.
  
  Returns:
      list: A list of evaluated results, including total loss value, data loss value, predicted scores, and ground-truth labels.
docstr_tok: ["Evaluate","the","data","in","`","feed_dict","`","with","current","model",".","Args",":","sess","(","object",")",":","The","model","session","object",".","feed_dict","(","dict",")",":","Feed","values","for","evaluation",".","This","is","a","dictionary","that","maps","graph","elements","to","values",".","Returns",":","list",":","A","list","of","evaluated","results",",","including","total","loss","value",",","data","loss","value",",","predicted","scores",",","and","ground-truth","labels","."]
code: def eval(self, sess, feed_dict):
      """Evaluate the data in `feed_dict` with current model.
  
      Args:
          sess (object): The model session object.
          feed_dict (dict): Feed values for evaluation. This is a dictionary that maps graph elements to values.
  
      Returns:
          list: A list of evaluated results, including total loss value, data loss value, predicted scores, and ground-truth labels.
      """
      feed_dict[self.layer_keeps] = self.keep_prob_test
      feed_dict[self.is_train_stage] = False
      return sess.run(
          [self.pred, self.iterator.labels], feed_dict=feed_dict
      )
  
imports: from os.path import join
  import abc
  import time
  import os
  import numpy
  import tensorflow
  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.eval(sess, feed_dict) and returns: sess