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 forward(self, x, normalize=True):
      x0 = self.patch_embed(x)
      x0 = self.pos_drop(x0)
      x0_out = self.proj_out(x0, normalize)
      if self.use_v2:
          x0 = self.layers1c[0](x0.contiguous())
      x1 = self.layers1[0](x0.contiguous())
      x1_out = self.proj_out(x1, normalize)
      if self.use_v2:
          x1 = self.layers2c[0](x1.contiguous())
      x2 = self.layers2[0](x1.contiguous())
      x2_out = self.proj_out(x2, normalize)
      if self.use_v2:
          x2 = self.layers3c[0](x2.contiguous())
      x3 = self.layers3[0](x2.contiguous())
      x3_out = self.proj_out(x3, normalize)
      if self.use_v2:
          x3 = self.layers4c[0](x3.contiguous())
      x4 = self.layers4[0](x3.contiguous())
      x4_out = self.proj_out(x4, normalize)
      return [x0_out, x1_out, x2_out, x3_out, x4_out]
  
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 3 for calling the SwinTransformer obj's underlying member method code with example usage: obj.forward(x, normalize) and returns: list