code: def __init__(self, dim, seq_len, depth, heads, mlp_mult, dropout=0.0):
      super().__init__()
      self.layers = nn.ModuleList([])
      self.pos_emb = nn.Parameter(torch.randn(seq_len))
  
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(dim, heads=heads, dropout=dropout),
                      FeedForward(dim, mlp_mult, dropout=dropout),
                  ]
              )
          )
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  from einops import rearrange
  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, seq_len, depth, heads, mlp_mult, dropout)