class_docstr: A basic Swin Transformer layer for one stage.
  Args:
      dim (int): Number of input channels.
      input_resolution (tuple[int]): Input resolution.
      depth (int): Number of blocks.
      num_heads (int): Number of attention heads.
      window_size (int): Local window size.
      mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default: 4
      qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
      qk_scale (float | None, optional): Override default query and key scale of head_dim ** -0.5 if set.
      drop (float, optional): Dropout rate. Default: 0.0
      attn_drop (float, optional): Attention dropout rate. Default: 0.0
      drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
      norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
      downsample (nn.Layer | None, optional): Downsample layer at the end of the layer. Default: None
class_docstr_tok: ["A","basic","Swin","Transformer","layer","for","one","stage",".","Args",":","dim","(","int",")",":","Number","of","input","channels",".","input_resolution","(","tuple","[","int","]",")",":","Input","resolution",".","depth","(","int",")",":","Number","of","blocks",".","num_heads","(","int",")",":","Number","of","attention","heads",".","window_size","(","int",")",":","Local","window","size",".","mlp_ratio","(","float",",","optional",")",":","Ratio","of","mlp","hidden","dim","to","embedding","dim",".","Default",":","4","qkv_bias","(","bool",",","optional",")",":","If","True",",","add","a","learnable","bias","to","query",",","key",",","value",".","Default",":","True","qk_scale","(","float","|","None",",","optional",")",":","Override","default","query","and","key","scale","of","head_dim","*","*","-0.5","if","set",".","drop","(","float",",","optional",")",":","Dropout","rate",".","Default",":","0.0","attn_drop","(","float",",","optional",")",":","Attention","dropout","rate",".","Default",":","0.0","drop_path","(","float","|","tuple","[","float","]",",","optional",")",":","Stochastic","depth","rate",".","Default",":","0.0","norm_layer","(","nn.Layer",",","optional",")",":","Normalization","layer",".","Default",":","nn.LayerNorm","downsample","(","nn.Layer","|","None",",","optional",")",":","Downsample","layer","at","the","end","of","the","layer",".","Default",":","None"]
code: def forward(self, x):
      for blk in self.blocks:
          x = blk(x)
      if self.downsample is not None:
          x = self.downsample(x)
      return x
  
imports: import numpy
  import paddle
  import paddle.nn
  from medicalseg.cvlibs import manager, param_init
  from medicalseg.utils import load_pretrained_model
  from medicalseg.models.backbones.transformer_utils import DropPath, to_2tuple
label_desc: Use image node_id 2 for calling the BasicLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x