class_docstr: Class for item-to-item recommendations using DKN.
  See https://github.com/microsoft/recommenders/blob/main/examples/07_tutorials/KDD2020-tutorial/step4_run_dkn_item2item.ipynb
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:
      numpy.ndarray, numpy.ndarray: A tuple with predictions and labels arrays.
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:
          numpy.ndarray, numpy.ndarray: A tuple with predictions and labels arrays.
      """
      feed_dict[self.layer_keeps] = self.keep_prob_test
      feed_dict[self.is_train_stage] = False
      preds = sess.run(self.pred, feed_dict=feed_dict)
      labels = np.zeros_like(preds, dtype=np.int32)
      labels[:, 0] = 1
      return (preds, labels)
  
file_path: recommenders/recommenders/models/deeprec/models/dkn_item2item.py
imports: import numpy
  import tensorflow
  from recommenders.models.deeprec.models.dkn import DKN
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 5 for calling the DKNItem2Item obj's underlying member method code with example usage: obj.eval(sess, feed_dict) and returns: preds,  labels