code: def norm_base(self) -> Callable:
      norm_type = self.norm_type
      norm_layer: Union[Type[Module], Any]
      if norm_type == "batch_norm":
          norm_layer = BN
      elif norm_type == "layer_norm":
          norm_layer = LN
      elif norm_type == "adain":
          norm_layer = AdaptiveInstanceNorm2d
      elif norm_type == "layer":
          norm_layer = nn.LayerNorm
      elif norm_type == "batch":
          norm_layer = nn.BatchNorm2d
      elif norm_type == "batch1d":
          norm_layer = nn.BatchNorm1d
      elif norm_type == "instance":
          norm_layer = nn.InstanceNorm2d
      elif norm_type == "spectral":
          norm_layer = torch.nn.utils.spectral_norm
      elif norm_type == "pixel":
          norm_layer = PixelNorm
      elif norm_type is None:
  
          def norm_layer(_: Any, *__: Any, **___: Any) -> nn.Identity:
              return nn.Identity()
  
      else:
          msg = f"normalization layer '{norm_type}' is not found"
          raise NotImplementedError(msg)
      return norm_layer
  
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 3 for calling the NormFactory obj's underlying member method code with example usage: obj.norm_base() and returns: norm_layer, nn