code: def __init__(
      self,
      dim,
      hidden_size,
      mlp_ratio=3.0,
      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__()
      channels_dim = int(mlp_ratio * dim)
      self.norm1 = norm_layer(dim)
      self.rnn_tokens = rnn_layer(
          dim,
          hidden_size,
          num_layers=num_layers,
          bidirectional=bidirectional,
          union=union,
          with_fc=with_fc,
      )
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
      self.norm2 = norm_layer(dim)
      self.mlp_channels = mlp_layer(
          dim, channels_dim, act_layer=act_layer, drop=drop
      )
  
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 Sequencer2dBlock object from inherited base classes: nn with example: obj = Sequencer2dBlock(dim, hidden_size, mlp_ratio, rnn_layer, mlp_layer, norm_layer, act_layer, num_layers, bidirectional, union, with_fc, drop, drop_path)