code: def __init__(
      self,
      dim,
      depth,
      heads=8,
      dim_head=64,
      mlp_mult=4,
      local_patch_size=7,
      global_k=7,
      dropout=0.0,
      has_local=True,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Residual(
                          LocalAttention(
                              dim,
                              heads=heads,
                              dim_head=dim_head,
                              dropout=dropout,
                              patch_size=local_patch_size,
                          )
                      )
                      if has_local
                      else nn.Identity(),
                      Residual(
                          FeedForward(dim, mlp_mult, dropout=dropout)
                      )
                      if has_local
                      else nn.Identity(),
                      Residual(
                          GlobalAttention(
                              dim,
                              heads=heads,
                              dim_head=dim_head,
                              dropout=dropout,
                              k=global_k,
                          )
                      ),
                      Residual(
                          FeedForward(dim, mlp_mult, dropout=dropout)
                      ),
                  ]
              )
          )
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(dim, depth, heads, dim_head, mlp_mult, local_patch_size, global_k, dropout, has_local)