code: def encode(self, state, src_seq, src_mask):
      # state: batch_size, state_dim
      # src_seq: batch_size, src_seq_len, dim_candidate
      # src_src_mask shape: batch_size, src_seq_len, src_seq_len
      batch_size = src_seq.shape[0]
  
      # candidate_embed: batch_size, src_seq_len, dim_model/2
      candidate_embed = self.candidate_embedder(src_seq)
      # state_embed: batch_size, dim_model/2
      state_embed = self.state_embedder(state)
      # transform state_embed into shape: batch_size, src_seq_len, dim_model/2
      state_embed = state_embed.repeat(1, self.max_src_seq_len).reshape(
          batch_size, self.max_src_seq_len, -1
      )
  
      # Input at each encoder step is actually concatenation of state_embed
      # and candidate embed. state_embed is replicated at each encoding step.
      # src_embed shape: batch_size, src_seq_len, dim_model
      src_embed = self.positional_encoding_encoder(
          torch.cat((state_embed, candidate_embed), dim=2)
      )
  
      # encoder_output shape: batch_size, src_seq_len, dim_model
      return self.encoder(src_embed, src_mask)
  
imports: import copy
  import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import types
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import subsequent_mask
  from reagent.models.base import ModelBase
  from reagent.models.seq2slate import Decoder, DecoderLayer, Embedder, Encoder, EncoderLayer, MultiHeadedAttention, PositionalEncoding, PositionwiseFeedForward
label_desc: Use image node_id 2 for calling the Seq2SlateTransformerRewardNet obj's underlying member method code with example usage: obj.encode(state, src_seq, src_mask) and returns: self