class_docstr: Layer normalization using mean and variance
  gamma and beta are the learnable parameters
docstr: Model forward pass.
  
  Args:
      x (tf.Tensor): Input tensor.
  
  Returns:
      tf.Tensor: Output tensor.
docstr_tok: ["Model","forward","pass",".","Args",":","x","(","tf.Tensor",")",":","Input","tensor",".","Returns",":","tf.Tensor",":","Output","tensor","."]
code: def call(self, x):
      """Model forward pass.
  
      Args:
          x (tf.Tensor): Input tensor.
  
      Returns:
          tf.Tensor: Output tensor.
      """
      mean, variance = tf.nn.moments(x, [-1], keepdims=True)
      normalized = (x - mean) / ((variance + self.epsilon) ** 0.5)
      output = self.gamma * normalized + self.beta
      return output
  
imports: import random
  import numpy
  from tqdm import tqdm
  import tensorflow
  from recommenders.utils.timer import Timer
label_desc: Use image node_id 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.call(x) and returns: output