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): Ratio of mlp hidden dim to embedding dim.
      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.
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code: def forward(self, x):
      for blk in self.blocks:
          if self.use_checkpoint:
              x = checkpoint.checkpoint(blk, x)
          else:
              x = blk(x)
      x_b4_ds = x
      if self.downsample is not None:
          x = self.downsample(x)
      return x_b4_ds, x
  
imports: import torch
  import torch.nn
  import torch.utils.checkpoint
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from math import sqrt
  from megatron import get_args
  from functools import partial
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_b4_ds,  x