code: def _create_sequencer2d(variant, pretrained=False, **kwargs):
      default_out_indices = tuple(range(3))
      out_indices = kwargs.pop("out_indices", default_out_indices)
  
      model = build_model_with_cfg(
          Sequencer2d,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          feature_cfg=dict(
              flatten_sequential=True, out_indices=out_indices
          ),
          **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 3 for calling a global function with example usage: _create_sequencer2d(variant, pretrained) and returns: model