code: def __init__(
      self,
      dim,
      depth,
      heads,
      dim_head,
      mlp_dim,
      dropout=0.0,
      patch_merge_layer=None,
      patch_merge_num_tokens=8,
  ):
      super().__init__()
      self.norm = nn.LayerNorm(dim)
      self.layers = nn.ModuleList([])
  
      self.patch_merge_layer_index = (
          default(patch_merge_layer, depth // 2) - 1
      )  # default to mid-way through transformer, as shown in paper
      self.patch_merger = PatchMerger(
          dim=dim, num_tokens_out=patch_merge_num_tokens
      )
  
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                      ),
                      FeedForward(dim, mlp_dim, dropout=dropout),
                  ]
              )
          )
  
imports: import torch
  from torch import nn
  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, heads, dim_head, mlp_dim, dropout, patch_merge_layer, patch_merge_num_tokens)