code: def vit_base_patch16_224(pretrained=False, **kwargs):
      """ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
      ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
      """
      assert not pretrained
      model_kwargs = dict(
          patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs
      )
      model = VisionTransformer(**model_kwargs)
      return model
  
imports: import math
  from functools import partial
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  from timm.models.helpers import named_apply
  from timm.models.layers import PatchEmbed
  from src.models.modules.mha import MHA
  from src.models.modules.mlp import Mlp, FusedDenseGeluDense
  from src.models.modules.block import Block
label_desc: Use image node_id 5 for calling a global function with example usage: vit_base_patch16_224(pretrained) and returns: model