class_docstr: A basic Swin Transformer layer for one stage.
  Args:
      dim (int): Number of feature channels
      depth (int): Depths of this stage.
      num_heads (int): Number of attention head.
      window_size (int): Local window size. Default: 7.
      mlp_ratio (float): 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 qk 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.Module, optional): Normalization layer. Default: nn.LayerNorm
      downsample (nn.Module | None, optional): Downsample layer at the end of the layer.
          Default: None
      use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
code: def __init__(
      self,
      dim,
      depth,
      num_heads,
      window_size=7,
      mlp_ratio=4.0,
      qkv_bias=True,
      qk_scale=None,
      drop=0.0,
      attn_drop=0.0,
      drop_path=0.0,
      norm_layer=nn.LayerNorm,
      downsample=None,
      use_checkpoint=False,
  ):
      super().__init__()
      self.window_size = window_size
      self.shift_size = window_size // 2
      self.depth = depth
      self.use_checkpoint = use_checkpoint
  
      # build blocks
      self.blocks = nn.ModuleList(
          [
              SwinTransformerBlock(
                  dim=dim,
                  num_heads=num_heads,
                  window_size=window_size,
                  shift_size=0 if (i % 2 == 0) else window_size // 2,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop,
                  attn_drop=attn_drop,
                  drop_path=drop_path[i]
                  if isinstance(drop_path, list)
                  else drop_path,
                  norm_layer=norm_layer,
              )
              for i in range(depth)
          ]
      )
  
      # patch merging layer
      if downsample is not None:
          self.downsample = downsample(dim=dim, norm_layer=norm_layer)
      else:
          self.downsample = None
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from detectron2.modeling.backbone.backbone import Backbone
label_desc: Use image node_id 1 to create a new BasicLayer object from inherited base classes: nn with example: obj = BasicLayer(dim, depth, num_heads, window_size, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, drop_path, norm_layer, downsample, use_checkpoint)