class_docstr: FrozenInTime model
  Args:
      img_size (int):
        the image or video height
      patch_size (int):
          patch height (equal to width)
      in_chans (int):
          number of image channel
      num_frames (int):
          maximum number of frames expected as input
      is_pretrained (bool):
          if true, then load the frozen pretrained model
      weights_path (str):
          the frozen pretrained model path, if is_pretrained is true, and this not None, then load from this path,
          otherwise, will load the frozen model from an url.
  
      projection_dim (int):
          the output dim
      video_pretrained_model (str):
          default is "vit_base_16x224", if is_pretrained is false, then must load the pretrained vit parameters
      video_is_load_pretrained (bool):
          if true, then will load vit pretrained weight
  
      text_pretrained_model (str):
          default is "distilbert-base-uncased", the pretrained model for text encoder
      text_is_load_pretrained (bool):
          if false, then will not load pretrained model for text encoder
      projection (str):
          a layer
code: def compute_video(
      self, video_data, question_embeddings, question_mask
  ):
      bridge_cls_embeddings, video_cls_embeddings = self.video_model(
          video_data, question_embeddings, question_mask
      )
  
      bridge_cls_embeddings = self.bridge_proj(bridge_cls_embeddings)
      video_cls_embeddings = self.vid_proj(video_cls_embeddings)
  
      return bridge_cls_embeddings, video_cls_embeddings
  
file_path: towhee/towhee/models/bridgeformer/bridge_former_training.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from transformers import AutoModel, AutoConfig
  from towhee.models.bridgeformer.bridge_former_training_block import VideoBridgeFormer
  from towhee.models import vit
  from towhee.models.frozen_in_time.frozen_utils import state_dict_data_parallel_fix
  import logging
label_desc: Use image node_id 4 for calling the BridgeFormerTraining obj's underlying member method code with example usage: obj.compute_video(video_data, question_embeddings, question_mask) and returns: bridge_cls_embeddings,  video_cls_embeddings