class_docstr: Reparametrization trick. Uses (z_mean, z_log_var) to sample the latent vector z. 
class_docstr_tok: ["Reparametrization","trick",".","Uses","(","z_mean",",","z_log_var",")","to","sample","the","latent","vector","z","."]
docstr: Sample z.
  
  Parameters
  ----------
  inputs
      Tuple with mean and log variance.
  
  Returns
  -------
  Sampled vector z.
docstr_tok: ["Sample","z.","Parameters","--","--","--","--","--","inputs","Tuple","with","mean","and","log","variance",".","Returns","--","--","--","-","Sampled","vector","z","."]
code: def call(self, inputs: Tuple[tf.Tensor, tf.Tensor]) -> tf.Tensor:
      """
      Sample z.
  
      Parameters
      ----------
      inputs
          Tuple with mean and log variance.
  
      Returns
      -------
      Sampled vector z.
      """
      z_mean, z_log_var = inputs
      batch = tf.shape(z_mean)[0]
      dim = tf.shape(z_mean)[1]
      epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
      return z_mean + tf.exp(0.5 * z_log_var) * epsilon
  
imports: import numpy
  import tensorflow
  from tensorflow.keras.layers import Bidirectional, Concatenate, Dense, Flatten, Layer, LSTM
  from typing import Callable, List, Tuple
  from alibi_detect.utils.tensorflow.distance import relative_euclidean_distance
label_desc: Use image node_id 1 for calling the Sampling obj's underlying member method code with example usage: obj.call(inputs) and returns: unknown