code: def call(self, x, training, mask):
      seq_len = tf.shape(x)[1]
      x = self.embedding(x)
      x *= tf.math.sqrt(tf.cast(self.embedding_size, tf.float32))
      x += self.pos_encoding[:, :seq_len, :]
      x = self.dropout(x, training=training)
      for i in range(self.num_layers):
          x = self.enc_layers[i](x, training, mask)
      return x
  
file_path: headliner/headliner/model/transformer_model.py
imports: from typing import Tuple
  from headliner.model.transformer_util import *
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