code: def __init__(
      self,
      dim,
      proj_kernel,
      kv_proj_stride,
      depth,
      heads,
      dim_head=64,
      mlp_mult=4,
      dropout=0.0,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          proj_kernel=proj_kernel,
                          kv_proj_stride=kv_proj_stride,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                      ),
                      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, proj_kernel, kv_proj_stride, depth, heads, dim_head, mlp_mult, dropout)