code: def __init__(
      self,
      dim,
      depth,
      heads,
      dim_head,
      mlp_dim,
      image_size,
      dropout=0.0,
      use_rotary=True,
      use_ds_conv=True,
      use_glu=True,
  ):
      super().__init__()
      self.layers = nn.ModuleList([])
      self.pos_emb = AxialRotaryEmbedding(dim_head, max_freq=image_size)
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                          use_rotary=use_rotary,
                          use_ds_conv=use_ds_conv,
                      ),
                      FeedForward(
                          dim, mlp_dim, dropout=dropout, use_glu=use_glu
                      ),
                  ]
              )
          )
  
imports: from math import sqrt, pi, log
  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, depth, heads, dim_head, mlp_dim, image_size, dropout, use_rotary, use_ds_conv, use_glu)