code: def __init__(
      self,
      base_dim,
      depth,
      heads,
      mlp_ratio,
      pool=None,
      proj_drop=0.0,
      attn_drop=0.0,
      drop_path_prob=None,
      norm_layer=None,
  ):
      super(Transformer, self).__init__()
      embed_dim = base_dim * heads
  
      self.pool = pool
      self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
      self.blocks = nn.Sequential(
          *[
              Block(
                  dim=embed_dim,
                  num_heads=heads,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=True,
                  proj_drop=proj_drop,
                  attn_drop=attn_drop,
                  drop_path=drop_path_prob[i],
                  norm_layer=partial(nn.LayerNorm, eps=1e-6),
              )
              for i in range(depth)
          ]
      )
  
imports: import math
  import re
  from functools import partial
  from typing import Sequence, Tuple
  import torch
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_, to_2tuple, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(base_dim, depth, heads, mlp_ratio, pool, proj_drop, attn_drop, drop_path_prob, norm_layer)