class_docstr: Swin UNETR based on: "Hatamizadeh et al.,
  Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
  <https://arxiv.org/abs/2201.01266>"
code: def forward(self, x_in):
      if not torch.jit.is_scripting():
          self._check_input_size(x_in.shape[2:])
      hidden_states_out = self.swinViT(x_in, self.normalize)
      enc0 = self.encoder1(x_in)
      enc1 = self.encoder2(hidden_states_out[0])
      enc2 = self.encoder3(hidden_states_out[1])
      enc3 = self.encoder4(hidden_states_out[2])
      dec4 = self.encoder10(hidden_states_out[4])
      dec3 = self.decoder5(dec4, hidden_states_out[3])
      dec2 = self.decoder4(dec3, enc3)
      dec1 = self.decoder3(dec2, enc2)
      dec0 = self.decoder2(dec1, enc1)
      out = self.decoder1(dec0, enc0)
      logits = self.out(out)
      return logits
  
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 4 for calling the SwinUNETR obj's underlying member method code with example usage: obj.forward(x_in) and returns: logits