code: def _create_eva(variant, pretrained=False, **kwargs):
      if kwargs.get("features_only", None):
          raise RuntimeError(
              "features_only not implemented for Eva models."
          )
  
      model = build_model_with_cfg(
          Eva,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          **kwargs,
      )
      return model
  
imports: import math
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
  from timm.layers import PatchEmbed, Mlp, GluMlp, SwiGLU, LayerNorm, DropPath, PatchDropout, RotaryEmbeddingCat, apply_rot_embed_cat, apply_keep_indices_nlc, trunc_normal_, resample_patch_embed, resample_abs_pos_embed, to_2tuple, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 2 for calling a global function with example usage: _create_eva(variant, pretrained) and returns: model