code: def _make_dinov2_model(
      *,
      arch_name: str = "vit_large",
      img_size: int = 518,
      patch_size: int = 14,
      init_values: float = 1.0,
      ffn_layer: str = "mlp",
      block_chunks: int = 0,
      num_register_tokens: int = 0,
      interpolate_antialias: bool = False,
      interpolate_offset: float = 0.1,
      pretrained: bool = True,
      weights: Union[Weights, str] = Weights.LVD142M,
      **kwargs,
  ):
      from ..models import vision_transformer as vits
  
      if isinstance(weights, str):
          try:
              weights = Weights[weights]
          except KeyError:
              raise AssertionError(f"Unsupported weights: {weights}")
  
      model_base_name = _make_dinov2_model_name(arch_name, patch_size)
      vit_kwargs = dict(
          img_size=img_size,
          patch_size=patch_size,
          init_values=init_values,
          ffn_layer=ffn_layer,
          block_chunks=block_chunks,
          num_register_tokens=num_register_tokens,
          interpolate_antialias=interpolate_antialias,
          interpolate_offset=interpolate_offset,
      )
      vit_kwargs.update(**kwargs)
      model = vits.__dict__[arch_name](**vit_kwargs)
  
      if pretrained:
          model_full_name = _make_dinov2_model_name(
              arch_name, patch_size, num_register_tokens
          )
          url = (
              _DINOV2_BASE_URL
              + f"/{model_base_name}/{model_full_name}_pretrain.pth"
          )
          state_dict = torch.hub.load_state_dict_from_url(
              url, map_location="cpu"
          )
          model.load_state_dict(state_dict, strict=True)
  
      return model
  
file_path: dinov2/dinov2/hub/backbones.py
imports: from enum import Enum
  from typing import Union
  import torch
  from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
label_desc: Use image node_id 1 for calling a global function with example usage: _make_dinov2_model() and returns: model