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 _check_input_size(self, spatial_shape):
      img_size = np.array(spatial_shape)
      remainder = (img_size % np.power(self.patch_size, 5)) > 0
      if remainder.any():
          wrong_dims = (np.where(remainder)[0] + 2).tolist()
          raise ValueError(
              f"spatial dimensions {wrong_dims} of input image (spatial shape: {spatial_shape})"
              f" must be divisible by {self.patch_size}**5."
          )
  
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 SwinUNETR obj's underlying member method code with example usage: obj._check_input_size(spatial_shape) without return types