code: def create_eva_vit_g(
      img_size=224,
      drop_path_rate=0.4,
      use_checkpoint=False,
      precision="fp16",
  ):
      model = VisionTransformer(
          img_size=img_size,
          patch_size=14,
          use_mean_pooling=False,
          embed_dim=1408,
          depth=39,
          num_heads=1408 // 88,
          mlp_ratio=4.3637,
          qkv_bias=True,
          drop_path_rate=drop_path_rate,
          norm_layer=partial(nn.LayerNorm, eps=1e-6),
          use_checkpoint=use_checkpoint,
      )
      url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth"
      cached_file = download_cached_file(
          url, check_hash=False, progress=True
      )
      state_dict = torch.load(cached_file, map_location="cpu")
      interpolate_pos_embed(model, state_dict)
  
      incompatible_keys = model.load_state_dict(
          state_dict, strict=False
      )
      #     print(incompatible_keys)
  
      if precision == "fp16":
          #         model.to("cuda")
          convert_weights_to_fp16(model)
      return model
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 4 for calling a global function with example usage: create_eva_vit_g(img_size, drop_path_rate, use_checkpoint, precision) and returns: model