code: def __init__(
      self,
      *,
      dim,
      depth,
      seq_len,
      causal=True,
      heads=8,
      dim_head=64,
      ff_mult=4,
      attn_dropout=0.0,
      ff_dropout=0.0,
      sparse_attn=False,
      sandwich_norm=False,
  ):
      super().__init__()
      layers = nn.ModuleList([])
      sparse_layer = cast_tuple(sparse_attn, depth)
  
      for ind, sparse_attn in zip(range(depth), sparse_layer):
          attn = Attention(
              dim,
              causal=causal,
              seq_len=seq_len,
              heads=heads,
              dim_head=dim_head,
              dropout=attn_dropout,
          )
  
          ff = FeedForward(dim, mult=ff_mult, dropout=ff_dropout)
  
          layers.append(
              nn.ModuleList(
                  [
                      LayerScale(
                          dim,
                          ind + 1,
                          PreNorm(dim, attn, sandwich=sandwich_norm),
                      ),
                      LayerScale(
                          dim,
                          ind + 1,
                          PreNorm(dim, ff, sandwich=sandwich_norm),
                      ),
                  ]
              )
          )
  
      execute_type = SequentialSequence
      route_attn = ((True, False),) * depth
      attn_route_map = {"mask": route_attn}
  
      self.layers = execute_type(layers, args_route=attn_route_map)
  
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import nn
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer()