code: def forward_features(self, x):
      # b, c, t, h, w = x.shape
      x = self.model.forward_features(x)
  
      ## apply pooling
      W = H = self.img_size // self.patch_size
      T = self.num_frames
  
      cls_tokens = x[:, 0, :].unsqueeze(1)
      other_tokens = x[:, 1:, :]
  
      x = rearrange(
          other_tokens, "b (h w t) m -> b t (h w) m", h=H, w=W, t=T
      )
  
      x = torch.mean(x, dim=1)
      x = torch.cat((cls_tokens, x), dim=1)
  
      return x
  
imports: import logging
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils
  import torch.utils.checkpoint
  from einops import rearrange
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
  from .helpers import load_pretrained, load_pretrained_imagenet, load_pretrained_kinetics
  from .vit_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DropPath, to_2tuple, trunc_normal_
label_desc: Use image node_id 3 for calling the TimeSformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: x