class_docstr: Multinomial Variational Autoencoders (Multi-VAE) for Collaborative Filtering implementation
  
  :Citation:
  
      Liang, Dawen, et al. "Variational autoencoders for collaborative filtering."
      Proceedings of the 2018 World Wide Web Conference. 2018.
      https://arxiv.org/pdf/1802.05814.pdf
docstr: Returns the value of the optimal beta.
code: def get_optimal_beta(self):
      """Returns the value of the optimal beta."""
      if self.annealing:
          # find the epoch/index that had the highest NDCG@k value
          index_max_ndcg = np.argmax(self.val_ndcg)
  
          # using this index find the value that beta had at this epoch
          return self.ls_beta[index_max_ndcg]
      else:
          return self.beta
  
imports: import numpy
  import matplotlib.pyplot
  import seaborn
  from recommenders.evaluation.python_evaluation import ndcg_at_k
  import tensorflow
  from tensorflow.keras.layers import *
  from tensorflow.keras.models import Model
  from tensorflow.keras import backend
  from tensorflow.keras.callbacks import ReduceLROnPlateau, Callback
label_desc: Use image node_id 7 for calling the Mult_VAE obj's underlying member method code with example usage: obj.get_optimal_beta() and returns: self, self