code: def encode(self,
                 data: mx.sym.Symbol,
                 data_length: mx.sym.Symbol,
                 seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]:
          
          data = utils.cast_conditionally(data, self.dtype)
          if self.config.dropout_prepost > 0.0:
              data = mx.sym.Dropout(data=data, p=self.config.dropout_prepost)
  
          # (batch_size * heads, 1, max_length)
          bias = mx.sym.expand_dims(transformer.get_valid_length_mask_for(data=data,
                                                                          lengths=data_length,
                                                                          num_heads=self.config.attention_heads,
                                                                          fold_heads=True,
                                                                          name="%sbias" % self.prefix), axis=1)
          bias = utils.cast_conditionally(bias, self.dtype)
          for i, layer in enumerate(self.layers):
              # (batch_size, seq_len, config.model_size)
              data = layer(data, bias)
          data = self.final_process(data=data, prev=None)
          data = utils.uncast_conditionally(data, self.dtype)
          return data, data_length, seq_len
docs: Encodes data given sequence lengths of individual examples and maximum sequence length.
  
          :param data: Input data.
          :param data_length: Vector with sequence lengths.
          :param seq_len: Maximum sequence length.
          :return: Encoded versions of input data data, data_length, seq_len.