code: def filter_swinunetr(key, value):
      """
      A filter function used to filter the pretrained weights from [1], then the weights can be loaded into MONAI SwinUNETR Model.
      This function is typically used with `monai.networks.copy_model_state`
      [1] "Valanarasu JM et al., Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
      <https://arxiv.org/abs/2307.16896>"
  
      Args:
          key: the key in the source state dict used for the update.
          value: the value in the source state dict used for the update.
  
      Examples::
  
          import torch
          from monai.apps import download_url
          from monai.networks.utils import copy_model_state
          from monai.networks.nets.swin_unetr import SwinUNETR, filter_swinunetr
  
          model = SwinUNETR(img_size=(96, 96, 96), in_channels=1, out_channels=3, feature_size=48)
          resource = (
              "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/ssl_pretrained_weights.pth"
          )
          ssl_weights_path = "./ssl_pretrained_weights.pth"
          download_url(resource, ssl_weights_path)
          ssl_weights = torch.load(ssl_weights_path)["model"]
  
          dst_dict, loaded, not_loaded = copy_model_state(model, ssl_weights, filter_func=filter_swinunetr)
  
      """
      if key in [
          "encoder.mask_token",
          "encoder.norm.weight",
          "encoder.norm.bias",
          "out.conv.conv.weight",
          "out.conv.conv.bias",
      ]:
          return None
  
      if key[:8] == "encoder.":
          if key[8:19] == "patch_embed":
              new_key = "swinViT." + key[8:]
          else:
              new_key = "swinViT." + key[8:18] + key[20:]
  
          return new_key, value
      else:
          return None
  
imports: from __future__ import annotations
  import itertools
  from collections.abc import Sequence
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.nn import LayerNorm
  from typing_extensions import Final
  from monai.networks.blocks import MLPBlock
  from monai.networks.blocks import PatchEmbed, UnetOutBlock, UnetrBasicBlock, UnetrUpBlock
  from monai.networks.layers import DropPath, trunc_normal_
  from monai.utils import ensure_tuple_rep, look_up_option, optional_import
  from monai.utils.deprecate_utils import deprecated_arg
label_desc: Use image node_id 5 for calling a global function with example usage: filter_swinunetr(key, value) and returns: None, new_key,  value, None