code: def gen_vit_config(config):
      num_patches = (config.image_size // config.patch_size) ** 2 + 1
      max_batch_size = 16
      vit_config = TransformerEncoderLayer.get_config(
          max_batch_tokens=num_patches * max_batch_size,
          max_seq_len=num_patches,
          hidden_size=config.hidden_size,
          intermediate_size=config.intermediate_size,
          nhead=config.num_attention_heads,
          attn_prob_dropout_ratio=config.attention_probs_dropout_prob,
          activation_dropout_ratio=config.hidden_dropout_prob,
          hidden_dropout_ratio=config.hidden_dropout_prob,
          pre_layer_norm=True,
          fp16=True,
          local_rank=0,
          activation_fn="gelu",
      )
      return vit_config
  
imports: import time
  import requests
  from PIL import Image
  import torch
  from transformers import ViTFeatureExtractor, ViTForImageClassification, ViTConfig
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerEncoderLayer
  from export.util import parse_args
label_desc: Use image node_id 6 for calling a global function with example usage: gen_vit_config(config) and returns: vit_config