recommenders/models/vae/multinomial_vae.py [195:262]:
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    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,
    ):

        """Constructor

        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 = (
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recommenders/models/vae/standard_vae.py [188:255]:
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    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 = (
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