code: def __init__(
      self,
      dim,
      depth,
      dim_head=32,
      heads=8,
      ff_mult=4,
      dropout=0.0,
      norm_output=True,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
  
      for ind in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      DSSA(
                          dim,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                      ),
                      FeedForward(dim, mult=ff_mult, dropout=dropout),
                  ]
              )
          )
  
      self.norm = ChanLayerNorm(dim) if norm_output else nn.Identity()
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange, Reduce
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(dim, depth, dim_head, heads, ff_mult, dropout, norm_output)