code: def encode_paragraph(
      self,
      paragraph_caption_vectors,
      paragraph_caption_mask,
      paragraph_caption_len,
      sentence_num,
      sentence_caption_vectors,
      sentence_caption_mask,
      sentence_caption_len,
  ):
      # compute paragraph context
      paragraph_context = self.text_pooler(
          paragraph_caption_vectors,
          paragraph_caption_mask,
          paragraph_caption_len,
          None,
      )
  
      if (
          self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.TEXT_SEQUENCER.USE_CONTEXT
      ):
          if (
              self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.TEXT_SEQUENCER.NAME
              == "rnn"
          ):
              paragraph_gru_hidden = paragraph_context.unsqueeze(0)
              paragraph_gru_hidden = paragraph_gru_hidden.repeat(
                  self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.TEXT_SEQUENCER.NUM_LAYERS,
                  1,
                  1,
              )
          elif (
              self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.TEXT_SEQUENCER.NAME
              == "atn"
          ):
              paragraph_gru_hidden = paragraph_context
          else:
              raise NotImplementedError
      else:
          paragraph_gru_hidden = None
  
      # compute sentence embedding
      sentence_emb = self.text_pooler(
          sentence_caption_vectors,
          sentence_caption_mask,
          sentence_caption_len,
          None,
      )
      batch_size = len(sentence_num)
      sentence_feat_dim = (
          self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.TEXT_POOLER.OUTPUT_DIM
      )
      max_sentence_len = torch.max(sentence_num)
      sentence_emb_reshape = torch.zeros(
          (batch_size, max_sentence_len, sentence_feat_dim)
      )
      sentence_emb_mask = torch.zeros((batch_size, max_sentence_len))
      sentence_emb_lens = torch.zeros((batch_size,))
      if self.use_cuda:
          sentence_emb_reshape = sentence_emb_reshape.cuda(
              non_blocking=True
          )
          sentence_emb_mask = sentence_emb_mask.cuda(non_blocking=True)
          sentence_emb_lens = sentence_emb_lens.cuda(non_blocking=True)
      pointer = 0
      for batch, sentence_len in enumerate(sentence_num):
          sentence_emb_reshape[batch, :sentence_len, :] = sentence_emb[
              pointer : pointer + sentence_len, :
          ]
          sentence_emb_mask[batch, :sentence_len] = 1
          sentence_emb_lens[batch] = sentence_len
          pointer += sentence_len
  
      # compute paragraph embedding
      paragraph_emb = self.text_sequencer(
          sentence_emb_reshape,
          sentence_emb_mask,
          sentence_num,
          paragraph_gru_hidden,
      )
  
      return (
          paragraph_emb,
          sentence_emb,
          paragraph_context,
          sentence_emb_reshape,
          sentence_emb_mask,
          sentence_emb_lens,
      )
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 3 for calling the MultiModalTransformer obj's underlying member method code with example usage: obj.encode_paragraph(paragraph_caption_vectors, paragraph_caption_mask, paragraph_caption_len, sentence_num, sentence_caption_vectors, sentence_caption_mask, sentence_caption_len) and returns: paragraph_emb,  sentence_emb,  paragraph_context,  sentence_emb_reshape,  sentence_emb_mask,  sentence_emb_lens