code: def load_state_dict(self, pretrained_ckpt_path):
      logging.info(
          "Loading TimeSformer checkpoints from {}".format(
              pretrained_ckpt_path
          )
      )
  
      if pretrained_ckpt_path == "vit_base_patch16_224":
          load_ckpt_func = load_pretrained_imagenet
      else:
          load_ckpt_func = load_pretrained_kinetics
  
      load_ckpt_func(
          self.model,
          num_classes=self.model.num_classes,
          in_chans=3,
          filter_fn=_conv_filter,
          img_size=self.img_size,
          num_frames=self.num_frames,
          num_patches=self.num_patches,
          attention_type=self.attention_type,
          pretrained_model=pretrained_ckpt_path,
      )
  
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 4 for calling the TimeSformer obj's underlying member method code with example usage: obj.load_state_dict(pretrained_ckpt_path) without return types