code: def __init__(
      self,
      dim,
      depth,
      max_tokens_per_depth,
      heads,
      dim_head,
      mlp_dim,
      dropout=0.0,
  ):
      super().__init__()
      assert (
          len(max_tokens_per_depth) == depth
      ), "max_tokens_per_depth must be a tuple of length that is equal to the depth of the transformer"
      assert sorted(max_tokens_per_depth, reverse=True) == list(
          max_tokens_per_depth
      ), "max_tokens_per_depth must be in decreasing order"
      assert (
          min(max_tokens_per_depth) > 0
      ), "max_tokens_per_depth must have at least 1 token at any layer"
  
      self.layers = nn.ModuleList([])
      for _, output_num_tokens in zip(
          range(depth), max_tokens_per_depth
      ):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          output_num_tokens=output_num_tokens,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                      ),
                      FeedForward(dim, mlp_dim, dropout=dropout),
                  ]
              )
          )
  
imports: import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from torch import nn, einsum
  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, max_tokens_per_depth, heads, dim_head, mlp_dim, dropout)