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.
      attention_style (`str`):
          Default is "frozen_in_time", if is "bridge_former", then is the BridgeFormer model.
      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.
      video_model_type (`str`) :
          Default is "SpaceTimeTransformer", the frozen model name, no need to change.
      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.
      load_temporal_fix (`str`):
          If is_pretrained is true, this will use in function "_inflate_positional_embeds".
      device (`str`):
          "cpu" or "cuda".
      pretrained_url (`str`):
          If is_pretrained is true and weights_path is None, will load frozen pretrained model from this url.
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code: def compute_video(self, video_data):
      video_embeddings = self.video_model(video_data)
      video_embeddings = self.vid_proj(video_embeddings)
      return video_embeddings
  
file_path: towhee/towhee/models/frozen_in_time/frozen_in_time.py
imports: import os
  import torch
  from torch import nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from transformers import AutoModel, AutoConfig
  from towhee.models.frozen_in_time.frozen_video_transformer import SpaceTimeTransformer
  from towhee.models import vit
  from towhee.models.frozen_in_time.frozen_utils import sim_matrix, state_dict_data_parallel_fix, remove_bridge_module_state_dic
  import logging
label_desc: Use image node_id 4 for calling the FrozenInTime obj's underlying member method code with example usage: obj.compute_video(video_data) and returns: video_embeddings