code: def __init__(
      self,
      dim,
      *,
      local_window_size,
      global_window_size,
      depth=4,
      dim_head=32,
      attn_dropout=0.0,
      ff_dropout=0.0,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
  
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          attn_type="short",
                          window_size=local_window_size,
                          dim_head=dim_head,
                          dropout=attn_dropout,
                      ),
                      FeedForward(dim, dropout=ff_dropout),
                      Attention(
                          dim,
                          attn_type="long",
                          window_size=global_window_size,
                          dim_head=dim_head,
                          dropout=attn_dropout,
                      ),
                      FeedForward(dim, dropout=ff_dropout),
                  ]
              )
          )
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(dim)