code: def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0):
      super().__init__()
      self.layers = nn.ModuleList([])
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Residual(
                          Attention(
                              dim,
                              heads=heads,
                              dim_head=dim_head,
                              dropout=dropout,
                          )
                      ),
                      ExcludeCLS(
                          Residual(
                              FeedForward(dim, mlp_dim, dropout=dropout)
                          )
                      ),
                  ]
              )
          )
  
imports: from math import sqrt
  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_dim, dropout)