class_docstr: Swin Transformer based on: "Liu et al.,
  Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
  <https://arxiv.org/abs/2103.14030>"
  https://github.com/microsoft/Swin-Transformer
code: def proj_out(self, x, normalize=False):
      if normalize:
          x_shape = x.size()
          if len(x_shape) == 5:
              n, ch, d, h, w = x_shape
              x = rearrange(x, "n c d h w -> n d h w c")
              x = F.layer_norm(x, [ch])
              x = rearrange(x, "n d h w c -> n c d h w")
          elif len(x_shape) == 4:
              n, ch, h, w = x_shape
              x = rearrange(x, "n c h w -> n h w c")
              x = F.layer_norm(x, [ch])
              x = rearrange(x, "n h w c -> n c h w")
      return x
  
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 2 for calling the SwinTransformer obj's underlying member method code with example usage: obj.proj_out(x, normalize) and returns: x