code: def inject_to(
      self,
      dim: int,
      norm_kwargs: Dict[str, Any],
      current_blocks: List[Module],
      *subsequent_blocks: Module,
  ) -> None:
      if self.norm_type != "spectral":
          new_block = self.make(dim, **norm_kwargs)
          current_blocks.append(new_block)
      else:
          last_block = current_blocks[-1]
          last_block = self.make(last_block, **norm_kwargs)
          current_blocks[-1] = last_block
      current_blocks.extend(subsequent_blocks)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Type
  from typing import Union
  from typing import Callable
  from typing import Optional
  from torch.nn import Module
  from cftool.misc import update_dict
  from ...misc.toolkit import adain_with_params
label_desc: Use image node_id 6 for calling the NormFactory obj's underlying member method code with example usage: obj.inject_to(dim, norm_kwargs, current_blocks) without return types