code: from torch import nn
  from Models.CRF import CRF
  from Models.Transformer import Transformer
  from Models.TransformerCtx import TransformerCtx
  from Models.SequenceEncoder import SequenceEncoder
  from Models.Attention import Attention
  import torch
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  
  
  class Transformer_CRF(nn.Module):
      def __init__(self, vocab_size, ctx_vocab_size, nb_labels, emb_dim, hidden_dim, bos_idx, eos_idx, pad_idx, num_lstm_layers, dropout, device):
          super().__init__()
          self.transformer = Transformer(
              vocab_size, in_dim=emb_dim, nb_labels=nb_labels, dropout=dropout
          )
          self.crf = CRF(
              nb_labels,
              device,
              bos_idx,
              eos_idx,
              pad_tag_id=pad_idx,
              batch_first=True,
          )
          self.ctx_encoder = TransformerCtx(ctx_vocab_size, device=device, in_dim=emb_dim)
          self.ctx_combiner = Attention(emb_dim)
          self.query = nn.Parameter(torch.Tensor(1, emb_dim))
          torch.nn.init.xavier_uniform_(self.query.data)
          self.emb_dim = emb_dim
          self.ctx_linear = nn.Linear(2 * emb_dim, emb_dim)
  
      def combine_ctx(self, x, before_ctx, after_ctx):
          # (batch, h_dim)
          before_ctx_encoded = self.before_ctx_encoder(before_ctx)
          after_ctx_encoded = self.after_ctx_encoder(after_ctx)
  
          # (batch, 2 * h_dim)
          ctx_cat = torch.cat((before_ctx_encoded, after_ctx_encoded), dim=1)
          # (batch, h_dim)
          encoded_ctx = torch.tanh(self.ctx_linear(ctx_cat))
  
          seq_len = x.shape[1]
  
          # (batch, seq_len, h_dim)
          encoded_ctx_repeated = encoded_ctx.unsqueeze(dim=0).repeat(seq_len, 1, 1)
          return encoded_ctx_repeated
  
      def forward_ctx(self, x, before_ctx, after_ctx):
          batch_size = x.shape[0]
          # (batch_size, 1, emb_dim)
          query = self.query.expand(batch_size, self.emb_dim).unsqueeze(dim=1)
          packed_query = pack_padded_sequence(query, batch_size * [1], batch_first=True, enforce_sorted=False)
          # Packed sequence (before_ctx_length, batch_size, emb_dim)
          encoded_before_ctx = self.ctx_encoder(before_ctx)
          # (batch_size, 1, emb_dim)
          encoded_before_ctx, _ = self.ctx_combiner(packed_query, encoded_before_ctx)
          # Packed sequence (after_ctx_length, batch_size, emb_dim)
          encoded_after_ctx = self.ctx_encoder(after_ctx)
          # (batch_size, 1 ,emb_dim)
          encoded_after_ctx, _ = self.ctx_combiner(packed_query, encoded_after_ctx)
          # (batch_size ,emb_dim)
          combined_ctx = self.ctx_linear(torch.cat([encoded_before_ctx, encoded_after_ctx], dim=2).squeeze())
          # (1, batch_size ,emb_dim)
          combined_ctx = combined_ctx.unsqueeze(dim=0)
          seq_len = x.shape[1]
          # (seq_len, batch_size, emb_dim)
          combined_ctx = combined_ctx.repeat(seq_len, 1, 1)
          return combined_ctx
  
      def forward(self, x, before_ctx, after_ctx, mask=None):
          # (seq_len, batch_size, emb_dim)
          combined_ctx = self.forward_ctx(x, before_ctx, after_ctx)
          # (batch_size, src_length, num_labels)
          emissions = self.transformer(x, combined_ctx, mask)
          score, path = self.crf.decode(emissions, mask=mask)
          return score, path
  
      def loss(self, x,  before_ctx, after_ctx, y, mask=None):
          # (seq_len, batch_size, emb_dim)
          combined_ctx = self.forward_ctx(x, before_ctx, after_ctx)
          # (batch_size, src_length, num_labels)
          emissions = self.transformer(x, combined_ctx, mask)
          nll = self.crf(emissions, y, mask=mask)
          return nll
  
apis: torch.nn.init.xavier_uniform_ torch.nn.Linear torch.nn.utils.rnn.pack_padded_sequence torch.cat torch.Tensor