code: def sequencer2d_l(pretrained=False, **kwargs) -> Sequencer2d:
      model_args = dict(
          layers=[8, 8, 16, 4],
          patch_sizes=[7, 2, 1, 1],
          embed_dims=[192, 384, 384, 384],
          hidden_sizes=[48, 96, 96, 96],
          mlp_ratios=[3.0, 3.0, 3.0, 3.0],
          rnn_layer=LSTM2d,
          bidirectional=True,
          union="cat",
          with_fc=True,
          **kwargs,
      )
      model = _create_sequencer2d(
          "sequencer2d_l",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
      return model
  
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 7 for calling a global function with example usage: sequencer2d_l(pretrained) and returns: model