class_docstr: Standard Variational Autoencoders (VAE) for Collaborative Filtering implementation.
docstr: Initialize class parameters.
  
  Args:
      n_users (int): Number of unique users in the train set.
      original_dim (int): Number of unique items in the train set.
      intermediate_dim (int): Dimension of intermediate space.
      latent_dim (int): Dimension of latent space.
      n_epochs (int): Number of epochs for training.
      batch_size (int): Batch size.
      k (int): number of top k items per user.
      verbose (int): Whether to show the training output or not.
      drop_encoder (float): Dropout percentage of the encoder.
      drop_decoder (float): Dropout percentage of the decoder.
      beta (float): a constant parameter β in the ELBO function,
            when you are not using annealing (annealing=False)
      annealing (bool): option of using annealing method for training the model (True)
            or not using annealing, keeping a constant beta (False)
      anneal_cap (float): maximum value that beta can take during annealing process.
      seed (int): Seed.
      save_path (str): Default path to save weights.
code: def __init__(
      self,
      n_users,
      original_dim,
      intermediate_dim=200,
      latent_dim=70,
      n_epochs=400,
      batch_size=100,
      k=100,
      verbose=1,
      drop_encoder=0.5,
      drop_decoder=0.5,
      beta=1.0,
      annealing=False,
      anneal_cap=1.0,
      seed=None,
      save_path=None,
  ):
      """Initialize class parameters.
  
      Args:
          n_users (int): Number of unique users in the train set.
          original_dim (int): Number of unique items in the train set.
          intermediate_dim (int): Dimension of intermediate space.
          latent_dim (int): Dimension of latent space.
          n_epochs (int): Number of epochs for training.
          batch_size (int): Batch size.
          k (int): number of top k items per user.
          verbose (int): Whether to show the training output or not.
          drop_encoder (float): Dropout percentage of the encoder.
          drop_decoder (float): Dropout percentage of the decoder.
          beta (float): a constant parameter β in the ELBO function,
                when you are not using annealing (annealing=False)
          annealing (bool): option of using annealing method for training the model (True)
                or not using annealing, keeping a constant beta (False)
          anneal_cap (float): maximum value that beta can take during annealing process.
          seed (int): Seed.
          save_path (str): Default path to save weights.
      """
      # Seed
      self.seed = seed
      np.random.seed(self.seed)
  
      # Parameters
      self.n_users = n_users
      self.original_dim = original_dim
      self.intermediate_dim = intermediate_dim
      self.latent_dim = latent_dim
      self.n_epochs = n_epochs
      self.batch_size = batch_size
      self.k = k
      self.verbose = verbose
  
      # Compute samples per epoch
      self.number_of_batches = self.n_users // self.batch_size
  
      # Annealing parameters
      self.anneal_cap = anneal_cap
      self.annealing = annealing
  
      if self.annealing:
          self.beta = K.variable(0.0)
      else:
          self.beta = beta
  
      # Compute total annealing steps
      self.total_anneal_steps = (
          self.number_of_batches
          * (self.n_epochs - int(self.n_epochs * 0.2))
      ) // self.anneal_cap
  
      # Dropout parameters
      self.drop_encoder = drop_encoder
      self.drop_decoder = drop_decoder
  
      # Path to save optimal model
      self.save_path = save_path
  
      # Create StandardVAE model
      self._create_model()
  
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 1 to create a new StandardVAE object  with example: obj = StandardVAE(n_users, original_dim, intermediate_dim, latent_dim, n_epochs, batch_size, k, verbose, drop_encoder, drop_decoder, beta, annealing, anneal_cap, seed, save_path)