class_docstr: Vision Transformer
  
  A PyTorch impl of : `Space-Time Transformer` from Frozen-in-time  - by Max Bain.
      https://arxiv.org/abs/2104.00650
  
  Based off:
   - ViT implementation from the timm library [https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py]
  lucidrains timesformer implementation [https://github.com/lucidrains/TimeSformer-pytorch].
  
  Notable differences:
   - allows for variable length input frames (<= num_frames)
   - allows for variable length input resolution  (<= (img_size, img_size)) [UNTESTED]
   - different attention block mechanism
class_docstr_tok: ["Vision","Transformer","A","PyTorch","impl","of",":","`","Space-Time","Transformer","`","from","Frozen-in-time","-","by","Max","Bain",".","https",":","\/\/arxiv.org\/abs\/2104.00650","Based","off",":","-","ViT","implementation","from","the","timm","library","[","https",":","\/\/github.com\/rwightman\/pytorch-image-models\/blob\/master\/timm\/models\/vision_transformer.py","]","lucidrains","timesformer","implementation","[","https",":","\/\/github.com\/lucidrains\/TimeSformer-pytorch","]",".","Notable","differences",":","-","allows","for","variable","length","input","frames","(","<","=","num_frames",")","-","allows","for","variable","length","input","resolution","(","<","=","(","img_size",",","img_size",")",")","[","UNTESTED","]","-","different","attention","block","mechanism"]
code: def no_weight_decay(self):
      return {"pos_embed", "cls_token"}
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  from einops import rearrange, repeat
  from timm.models.layers import DropPath, trunc_normal_
  from torch import einsum, nn
  from towhee.models.layers.mlp import Mlp
  from towhee.models.layers.patch_embed2d import PatchEmbed2D
  from towhee.models.utils.init_vit_weights import init_vit_weights
  import logging
label_desc: Use image node_id 2 for calling the SpaceTimeTransformer obj's underlying member method code with example usage: obj.no_weight_decay() and returns: unknown