code: def davit_large(pretrained=False, **kwargs) -> DaVit:
      model_args = dict(
          depths=(1, 1, 9, 1),
          embed_dims=(192, 384, 768, 1536),
          num_heads=(6, 12, 24, 48),
      )
      return _create_davit(
          "davit_large",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
  
imports: from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, Mlp, LayerNorm2d, get_norm_layer, use_fused_attn
  from timm.layers import NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 9 for calling a global function with example usage: davit_large(pretrained) and returns: _create_davit