code: def __init__(
      self,
      dim,
      depth,
      heads,
      dim_head,
      mlp_dim,
      num_parallel_branches=2,
      dropout=0.0,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
  
      attn_block = lambda: Attention(
          dim, heads=heads, dim_head=dim_head, dropout=dropout
      )
      ff_block = lambda: FeedForward(dim, mlp_dim, dropout=dropout)
  
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Parallel(
                          *[
                              attn_block()
                              for _ in range(num_parallel_branches)
                          ]
                      ),
                      Parallel(
                          *[
                              ff_block()
                              for _ in range(num_parallel_branches)
                          ]
                      ),
                  ]
              )
          )
  
imports: import torch
  from torch import nn
  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, num_parallel_branches, dropout)