class_docstr: Standard Variational Autoencoders (VAE) for Collaborative Filtering implementation.
docstr: Sample epsilon ∼ N (0,I) and compute z via reparametrization trick.
code: def _take_sample(self, args):
      """Sample epsilon ∼ N (0,I) and compute z via reparametrization trick."""
      """Calculate latent vector using the reparametrization trick.
             The idea is that sampling from N (_mean, _var) is s the same as sampling from _mean+ epsilon * _var
             where epsilon ∼ N(0,I)."""
      # sampling from latent dimension for decoder/generative part of network
      _mean, _log_var = args
      epsilon = K.random_normal(
          shape=(K.shape(_mean)[0], self.latent_dim),
          mean=0.0,
          stddev=1.0,
          seed=self.seed,
      )
  
      return _mean + K.exp(_log_var / 2) * epsilon
  
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 4 for calling the StandardVAE obj's underlying member method code with example usage: obj._take_sample(args) and returns: unknown