class_docstr: A basic Swin Transformer layer for one stage.
      
class_docstr_tok: ["A","basic","Swin","Transformer","layer","for","one","stage","."]
code: def forward(self, x):
      x = self.downsample(x)
  
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = checkpoint_seq(self.blocks, x)
      else:
          x = self.blocks(x)
      return x
  
imports: import logging
  import math
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, _assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
  from .vision_transformer import get_init_weights_vit
label_desc: Use image node_id 2 for calling the SwinTransformerStage obj's underlying member method code with example usage: obj.forward(x) and returns: x