code: def checkpoint_filter_fn(state_dict, model):
      """convert patch embedding weight from manual patchify + linear proj to conv"""
      old_weights = True
      if "head.fc.weight" in state_dict:
          old_weights = False
      import re
  
      out_dict = {}
      state_dict = state_dict.get("model", state_dict)
      state_dict = state_dict.get("state_dict", state_dict)
      for k, v in state_dict.items():
          if any(
              [n in k for n in ("relative_position_index", "attn_mask")]
          ):
              continue  # skip buffers that should not be persistent
  
          if "patch_embed.proj.weight" in k:
              _, _, H, W = model.patch_embed.proj.weight.shape
              if v.shape[-2] != H or v.shape[-1] != W:
                  v = resample_patch_embed(
                      v,
                      (H, W),
                      interpolation="bicubic",
                      antialias=True,
                      verbose=True,
                  )
  
          if k.endswith("relative_position_bias_table"):
              m = model.get_submodule(k[:-29])
              if (
                  v.shape != m.relative_position_bias_table.shape
                  or m.window_size[0] != m.window_size[1]
              ):
                  v = resize_rel_pos_bias_table(
                      v,
                      new_window_size=m.window_size,
                      new_bias_shape=m.relative_position_bias_table.shape,
                  )
  
          if old_weights:
              k = re.sub(
                  r"layers.(\d+).downsample",
                  lambda x: f"layers.{int(x.group(1)) + 1}.downsample",
                  k,
              )
              k = k.replace("head.", "head.fc.")
  
          out_dict[k] = v
      return out_dict
  
imports: import logging
  import math
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, _assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
  from .vision_transformer import get_init_weights_vit
label_desc: Use image node_id 4 for calling a global function with example usage: checkpoint_filter_fn(state_dict, model) and returns: out_dict