code: def __init__(
      self,
      dim,
      dim_out,
      depth,
      patch_size,
      hidden_size,
      mlp_ratio,
      downsample=False,
      block_layer=Sequencer2dBlock,
      rnn_layer=LSTM2d,
      mlp_layer=Mlp,
      norm_layer=partial(nn.LayerNorm, eps=1e-6),
      act_layer=nn.GELU,
      num_layers=1,
      bidirectional=True,
      union="cat",
      with_fc=True,
      drop=0.0,
      drop_path=0.0,
  ):
      super().__init__()
      if downsample:
          self.downsample = Downsample2d(dim, dim_out, patch_size)
      else:
          assert dim == dim_out
          self.downsample = nn.Identity()
  
      blocks = []
      for block_idx in range(depth):
          blocks.append(
              block_layer(
                  dim_out,
                  hidden_size,
                  mlp_ratio=mlp_ratio,
                  rnn_layer=rnn_layer,
                  mlp_layer=mlp_layer,
                  norm_layer=norm_layer,
                  act_layer=act_layer,
                  num_layers=num_layers,
                  bidirectional=bidirectional,
                  union=union,
                  with_fc=with_fc,
                  drop=drop,
                  drop_path=drop_path[block_idx]
                  if isinstance(drop_path, (list, tuple))
                  else drop_path,
              )
          )
      self.blocks = nn.Sequential(*blocks)
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new Sequencer2dStage object from inherited base classes: nn with example: obj = Sequencer2dStage(dim, dim_out, depth, patch_size, hidden_size, mlp_ratio, downsample, block_layer, rnn_layer, mlp_layer, norm_layer, act_layer, num_layers, bidirectional, union, with_fc, drop, drop_path)