class_docstr: Swin Transformer
      A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
        https://arxiv.org/pdf/2103.14030
  
  Args:
      img_size (int | tuple(int)): Input image size. Default 224
      patch_size (int | tuple(int)): Patch size. Default: 4
      in_chans (int): Number of input image channels. Default: 3
      embed_dim (int): Patch embedding dimension. Default: 96
      depths (tuple(int)): Depth of each Swin Transformer layer.
      num_heads (tuple(int)): Number of attention heads in different layers.
      window_size (int): Window size. Default: 7
      mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
      qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
      qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
      drop_rate (float): Dropout rate. Default: 0
      attn_drop_rate (float): Attention dropout rate. Default: 0
      drop_path_rate (float): Stochastic depth rate. Default: 0.1
      norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
      ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
      patch_norm (bool): If True, add normalization after patch embedding. Default: True
      use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
code: def __init__(
      self,
      img_size=224,
      patch_size=4,
      in_chans=3,
      embed_dim=96,
      depths: Optional[List[int]] = None,
      num_heads: Optional[List[int]] = None,
      window_size=7,
      mlp_ratio=4.0,
      qkv_bias=True,
      qk_scale=None,
      drop_rate=0.0,
      attn_drop_rate=0.0,
      drop_path_rate=0.1,
      norm_layer=nn.LayerNorm,
      ape=False,
      patch_norm=True,
      use_checkpoint=False,
      **kwargs,
  ):
      super().__init__()
      depths = depths or [2, 2, 6, 2]
      num_heads = num_heads or [3, 6, 12, 24]
  
      self.num_layers = len(depths)
      self.embed_dim = embed_dim
      self.ape = ape
      self.patch_norm = patch_norm
      self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
      self.mlp_ratio = mlp_ratio
  
      # split image into non-overlapping patches
      self.patch_embed = PatchEmbed(
          img_size=img_size,
          patch_size=patch_size,
          in_chans=in_chans,
          embed_dim=embed_dim,
          norm_layer=norm_layer if self.patch_norm else None,
      )
      num_patches = self.patch_embed.num_patches
      patches_resolution = self.patch_embed.patches_resolution
      self.patches_resolution = patches_resolution
  
      # absolute position embedding
      if self.ape:
          self.absolute_pos_embed = nn.Parameter(
              torch.zeros(1, num_patches, embed_dim)
          )
          trunc_normal_(self.absolute_pos_embed, std=0.02)
  
      self.pos_drop = nn.Dropout(p=drop_rate)
  
      # stochastic depth
      dpr = [
          x.item()
          for x in torch.linspace(0, drop_path_rate, sum(depths))
      ]  # stochastic depth decay rule
  
      # build layers
      self.layers = nn.ModuleList()
      for i_layer in range(self.num_layers):
          layer = BasicLayer(
              dim=int(embed_dim * 2**i_layer),
              input_resolution=(
                  patches_resolution[0] // (2**i_layer),
                  patches_resolution[1] // (2**i_layer),
              ),
              depth=depths[i_layer],
              num_heads=num_heads[i_layer],
              window_size=window_size,
              mlp_ratio=self.mlp_ratio,
              qkv_bias=qkv_bias,
              qk_scale=qk_scale,
              drop=drop_rate,
              attn_drop=attn_drop_rate,
              drop_path=dpr[
                  sum(depths[:i_layer]) : sum(depths[: i_layer + 1])
              ],
              norm_layer=norm_layer,
              downsample=PatchMerging
              if (i_layer < self.num_layers - 1)
              else None,
              use_checkpoint=use_checkpoint,
          )
          self.layers.append(layer)
  
      self.norm = norm_layer(self.num_features)
      self.avgpool = nn.AdaptiveAvgPool1d(1)
      self.apply(self._init_weights)
  
imports: import math
  from functools import lru_cache
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, rearrange, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 1 to create a new SwinTransformer object from inherited base classes: nn with example: obj = SwinTransformer(img_size, patch_size, in_chans, embed_dim, depths, num_heads, window_size, mlp_ratio, qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate, norm_layer, ape, patch_norm, use_checkpoint)