class_docstr: Invokes Transformer based encoder with user defined number of layers
docstr: Model forward pass.
  
  Args:
      x (tf.Tensor): Input tensor.
      training (tf.Tensor): Training tensor.
      mask (tf.Tensor): Mask tensor.
  
  Returns:
      tf.Tensor: Output tensor.
docstr_tok: ["Model","forward","pass",".","Args",":","x","(","tf.Tensor",")",":","Input","tensor",".","training","(","tf.Tensor",")",":","Training","tensor",".","mask","(","tf.Tensor",")",":","Mask","tensor",".","Returns",":","tf.Tensor",":","Output","tensor","."]
code: def call(self, x, training, mask):
      """Model forward pass.
  
      Args:
          x (tf.Tensor): Input tensor.
          training (tf.Tensor): Training tensor.
          mask (tf.Tensor): Mask tensor.
  
      Returns:
          tf.Tensor: Output tensor.
      """
  
      for i in range(self.num_layers):
          x = self.enc_layers[i](x, training, mask)
  
      return x
  
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 Encoder obj's underlying member method code with example usage: obj.call(x, training, mask) and returns: x