code: def encode_video(
      self,
      vid_frames,
      vid_frames_mask,
      vid_frames_len,
      clip_num,
      clip_frames,
      clip_frames_len,
      clip_frames_mask,
  ):
      # compute video context
      vid_context = self.video_pooler(
          vid_frames, vid_frames_mask, vid_frames_len, None
      )
      if (
          self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.VIDEO_SEQUENCER.USE_CONTEXT
      ):
          if (
              self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.VIDEO_SEQUENCER.NAME
              == "rnn"
          ):
              vid_context_hidden = vid_context.unsqueeze(0)
              vid_context_hidden = vid_context_hidden.repeat(
                  self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.VIDEO_SEQUENCER.NUM_LAYERS,
                  1,
                  1,
              )
          elif (
              self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.VIDEO_SEQUENCER.NAME
              == "atn"
          ):
              vid_context_hidden = vid_context
          else:
              raise NotImplementedError
      else:
          vid_context_hidden = None
  
      # compute clip embedding
      clip_emb = self.video_pooler(
          clip_frames, clip_frames_mask, clip_frames_len, None
      )
      batch_size = len(clip_num)
      max_clip_len = torch.max(clip_num)
      clip_feat_dim = (
          self.cfg.CONFIG.COOT_MODEL.MODEL_CONFIG.VIDEO_POOLER.OUTPUT_DIM
      )
      clip_emb_reshape = torch.zeros(
          (batch_size, max_clip_len, clip_feat_dim)
      )
      clip_emb_mask = torch.zeros((batch_size, max_clip_len))
      clip_emb_lens = torch.zeros((batch_size,))
      if self.use_cuda:
          clip_emb_reshape = clip_emb_reshape.cuda(non_blocking=True)
          clip_emb_mask = clip_emb_mask.cuda(non_blocking=True)
          clip_emb_lens = clip_emb_lens.cuda(non_blocking=True)
      pointer = 0
      for batch, clip_len in enumerate(clip_num):
          clip_emb_reshape[batch, :clip_len, :] = clip_emb[
              pointer : pointer + clip_len, :
          ]
          clip_emb_mask[batch, :clip_len] = 1
          clip_emb_lens[batch] = clip_len
          pointer += clip_len
  
      # compute video embedding
      vid_emb = self.video_sequencer(
          clip_emb_reshape, clip_emb_mask, clip_num, vid_context_hidden
      )
  
      # TODO: convert the return to an object class or maybe dictionary
      return (
          vid_emb,
          clip_emb,
          vid_context,
          clip_emb_reshape,
          clip_emb_mask,
          clip_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 2 for calling the MultiModalTransformer obj's underlying member method code with example usage: obj.encode_video(vid_frames, vid_frames_mask, vid_frames_len, clip_num, clip_frames, clip_frames_len, clip_frames_mask) and returns: vid_emb,  clip_emb,  vid_context,  clip_emb_reshape,  clip_emb_mask,  clip_emb_lens