code: def get_hf_gpt_enc_layer_params(layer, gpt_config):
      init_ws = []
      init_bs = []
  
      init_ws.extend(
          layer.attn.c_attn.weight.detach()
          .clone()
          .t()
          .split(gpt_config.hidden_size, 0)
      )
      init_bs.extend(
          layer.attn.c_attn.bias.detach()
          .clone()
          .split(gpt_config.hidden_size, 0)
      )
  
      init_ws.append(
          layer.attn.c_proj.weight.detach().clone().t().reshape(-1)
      )
      init_bs.append(layer.attn.c_proj.bias.detach().clone())
      init_ws.append(layer.ln_1.weight.detach().clone())
      init_bs.append(layer.ln_1.bias.detach().clone())
  
      init_ws.append(
          layer.mlp.c_fc.weight.detach().clone().t().reshape(-1)
      )
      init_bs.append(layer.mlp.c_fc.bias.detach().clone())
      init_ws.append(
          layer.mlp.c_proj.weight.detach().clone().t().reshape(-1)
      )
      init_bs.append(layer.mlp.c_proj.bias.detach().clone())
      init_ws.append(layer.ln_2.weight.detach().clone())
      init_bs.append(layer.ln_2.bias.detach().clone())
  
      act_cmax = act_quant_config.amax.tolist()
      wei_cmax = weight_quant_config.amax.tolist()
      init_clip_max = torch.tensor([act_cmax, wei_cmax, act_cmax] * 4)
      init_ws.append(init_clip_max)
  
      return init_ws, init_bs
  
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 2 for calling a global function with example usage: get_hf_gpt_enc_layer_params(layer, gpt_config) and returns: init_ws,  init_bs