code: def __init__(
      self,
      num_classes=1000,
      img_size=224,
      in_chans=3,
      global_pool="avg",
      layers=(4, 3, 8, 3),
      patch_sizes=(7, 2, 2, 1),
      embed_dims=(192, 384, 384, 384),
      hidden_sizes=(48, 96, 96, 96),
      mlp_ratios=(3.0, 3.0, 3.0, 3.0),
      block_layer=Sequencer2dBlock,
      rnn_layer=LSTM2d,
      mlp_layer=Mlp,
      norm_layer=partial(nn.LayerNorm, eps=1e-6),
      act_layer=nn.GELU,
      num_rnn_layers=1,
      bidirectional=True,
      union="cat",
      with_fc=True,
      drop_rate=0.0,
      drop_path_rate=0.0,
      nlhb=False,
      stem_norm=False,
  ):
      super().__init__()
      assert global_pool in ("", "avg")
      self.num_classes = num_classes
      self.global_pool = global_pool
      self.num_features = embed_dims[
          -1
      ]  # num_features for consistency with other models
      self.feature_dim = (
          -1
      )  # channel dim index for feature outputs (rank 4, NHWC)
      self.output_fmt = "NHWC"
      self.feature_info = []
  
      self.stem = PatchEmbed(
          img_size=None,
          patch_size=patch_sizes[0],
          in_chans=in_chans,
          embed_dim=embed_dims[0],
          norm_layer=norm_layer if stem_norm else None,
          flatten=False,
          output_fmt="NHWC",
      )
  
      assert (
          len(layers)
          == len(patch_sizes)
          == len(embed_dims)
          == len(hidden_sizes)
          == len(mlp_ratios)
      )
      reductions = list(accumulate(patch_sizes, lambda x, y: x * y))
      stages = []
      prev_dim = embed_dims[0]
      for i, _ in enumerate(embed_dims):
          stages += [
              Sequencer2dStage(
                  prev_dim,
                  embed_dims[i],
                  depth=layers[i],
                  downsample=i > 0,
                  patch_size=patch_sizes[i],
                  hidden_size=hidden_sizes[i],
                  mlp_ratio=mlp_ratios[i],
                  block_layer=block_layer,
                  rnn_layer=rnn_layer,
                  mlp_layer=mlp_layer,
                  norm_layer=norm_layer,
                  act_layer=act_layer,
                  num_layers=num_rnn_layers,
                  bidirectional=bidirectional,
                  union=union,
                  with_fc=with_fc,
                  drop=drop_rate,
                  drop_path=drop_path_rate,
              )
          ]
          prev_dim = embed_dims[i]
          self.feature_info += [
              dict(
                  num_chs=prev_dim,
                  reduction=reductions[i],
                  module=f"stages.{i}",
              )
          ]
  
      self.stages = nn.Sequential(*stages)
      self.norm = norm_layer(embed_dims[-1])
      self.head = ClassifierHead(
          self.num_features,
          num_classes,
          pool_type=global_pool,
          drop_rate=drop_rate,
          input_fmt=self.output_fmt,
      )
  
      self.init_weights(nlhb=nlhb)
  
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 Sequencer2d object from inherited base classes: nn with example: obj = Sequencer2d(num_classes, img_size, in_chans, global_pool, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer, norm_layer, act_layer, num_rnn_layers, bidirectional, union, with_fc, drop_rate, drop_path_rate, nlhb, stem_norm)