code: def samvit_huge_patch16(
      pretrained=False, **kwargs
  ) -> VisionTransformerSAM:
      """ViT-H/16 for Segment-Anything"""
      model_args = dict(
          patch_size=16,
          embed_dim=1280,
          depth=32,
          num_heads=16,
          global_attn_indexes=[7, 15, 23, 31],
          window_size=14,
          use_rel_pos=True,
          img_size=1024,
      )
      model = _create_vision_transformer(
          "samvit_huge_patch16",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
      return model
  
imports: import logging
  from functools import partial
  from typing import Callable, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.jit import Final
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, PatchDropout, LayerNorm2d, ClassifierHead, NormMlpClassifierHead, Format, resample_abs_pos_embed_nhwc, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
  from ._features_fx import register_notrace_function
label_desc: Use image node_id 10 for calling a global function with example usage: samvit_huge_patch16(pretrained) and returns: model