class_docstr: Swin Transformer backbone
  Args:
      arch (str): Architecture of FocalNet
      pretrain_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.Layer): 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
code: def __init__(
      self,
      arch="swin_T_224",
      pretrain_img_size=224,
      patch_size=4,
      in_chans=3,
      embed_dim=96,
      depths=[2, 2, 6, 2],
      num_heads=[3, 6, 12, 24],
      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=0.2,
      norm_layer=nn.LayerNorm,
      ape=False,
      patch_norm=True,
      out_indices=(0, 1, 2, 3),
      frozen_stages=-1,
      pretrained=None,
  ):
      super(SwinTransformer, self).__init__()
      assert arch in MODEL_cfg.keys(), "Unsupported arch: {}".format(
          arch
      )
  
      pretrain_img_size = MODEL_cfg[arch]["pretrain_img_size"]
      embed_dim = MODEL_cfg[arch]["embed_dim"]
      depths = MODEL_cfg[arch]["depths"]
      num_heads = MODEL_cfg[arch]["num_heads"]
      window_size = MODEL_cfg[arch]["window_size"]
      if pretrained is None:
          pretrained = MODEL_cfg[arch]["pretrained"]
  
      self.num_layers = len(depths)
      self.ape = ape
      self.patch_norm = patch_norm
      self.out_indices = out_indices
      self.frozen_stages = frozen_stages
  
      # split image into non-overlapping patches
      self.patch_embed = PatchEmbed(
          patch_size=patch_size,
          in_chans=in_chans,
          embed_dim=embed_dim,
          norm_layer=norm_layer if self.patch_norm else None,
      )
  
      # absolute position embedding
      if self.ape:
          pretrain_img_size = to_2tuple(pretrain_img_size)
          patch_size = to_2tuple(patch_size)
          patches_resolution = [
              pretrain_img_size[0] // patch_size[0],
              pretrain_img_size[1] // patch_size[1],
          ]
  
          self.absolute_pos_embed = add_parameter(
              self,
              paddle.zeros(
                  (
                      1,
                      embed_dim,
                      patches_resolution[0],
                      patches_resolution[1],
                  )
              ),
          )
          trunc_normal_(self.absolute_pos_embed)
  
      self.pos_drop = nn.Dropout(p=drop_rate)
  
      # stochastic depth
      dpr = np.linspace(
          0, drop_path_rate, sum(depths)
      )  # stochastic depth decay rule
  
      # build layers
      self.layers = nn.LayerList()
      for i_layer in range(self.num_layers):
          layer = BasicLayer(
              dim=int(embed_dim * 2**i_layer),
              depth=depths[i_layer],
              num_heads=num_heads[i_layer],
              window_size=window_size,
              mlp_ratio=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,
          )
          self.layers.append(layer)
  
      num_features = [
          int(embed_dim * 2**i) for i in range(self.num_layers)
      ]
      self.num_features = num_features
  
      # add a norm layer for each output
      for i_layer in out_indices:
          layer = norm_layer(num_features[i_layer])
          layer_name = f"norm{i_layer}"
          self.add_sublayer(layer_name, layer)
  
      self.apply(self._init_weights)
      self._freeze_stages()
      if pretrained:
          if "http" in pretrained:  # URL
              path = paddle.utils.download.get_weights_path_from_url(
                  pretrained
              )
          else:  # model in local path
              path = pretrained
          self.set_state_dict(paddle.load(path))
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import DropPath, Identity
  from .transformer_utils import add_parameter, to_2tuple
  from .transformer_utils import ones_, zeros_, trunc_normal_
label_desc: Use image node_id 1 to create a new SwinTransformer object from inherited base classes: nn with example: obj = SwinTransformer(arch, pretrain_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, out_indices, frozen_stages, pretrained)