class_docstr: Standard Variational Autoencoders (VAE) for Collaborative Filtering implementation.
docstr: Returns the value of the optimal beta.
code: def get_optimal_beta(self):
      """Returns the value of the optimal beta."""
      # 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
      optimal_beta = self.ls_beta[index_max_ndcg]
  
      return optimal_beta
  
imports: import numpy
  import matplotlib.pyplot
  import seaborn
  import tensorflow
  from tensorflow.keras.layers import *
  from tensorflow.keras.models import Model
  from tensorflow.keras.losses import binary_crossentropy
  from tensorflow.keras import backend
  from tensorflow.keras.callbacks import ReduceLROnPlateau, Callback
  from recommenders.evaluation.python_evaluation import ndcg_at_k
label_desc: Use image node_id 7 for calling the StandardVAE obj's underlying member method code with example usage: obj.get_optimal_beta() and returns: optimal_beta