code: def ls_hf_gpt_enc_convert(model, training_args, config):
      for i in range(config.num_hidden_layers):
          model.transformer.h[i] = LSHFGptEncoderLayer.from_huggingface(
              model.transformer.h[i], training_args, config
          ).cuda()
  
file_path: lightseq/lightseq/training/ops/pytorch/gpt_layer.py
imports: import torch
  from lightseq.training.ops.pytorch import TransformerBuilder
  from lightseq.training.ops.pytorch.transformer_encoder_layer import LSTransformerEncoderLayer
  from lightseq.training.ops.pytorch.quantization import weight_quant_config, act_quant_config
label_desc: Use image node_id 3 for calling a global function with example usage: ls_hf_gpt_enc_convert(model, training_args, config) without return types