code: def get_hf_gpt_enc_layer_params(layer, config):
      init_ws = []
      init_bs = []
  
      init_ws.extend(
          layer.attn.c_attn.weight.detach()
          .clone()
          .t()
          .split(config.hidden_size, 0)
      )
      init_bs.extend(
          layer.attn.c_attn.bias.detach()
          .clone()
          .split(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())
  
      return init_ws, init_bs
  
file_path: lightseq/examples/training/huggingface/gpt/ls_hf_gpt_layer.py
imports: from torch import nn
  from lightseq.training.ops.pytorch.quantization import qat_mode, enable_quant, disable_quant, QuantLinear, TensorQuantizer, emb_quant_config
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerDecoderLayer, copy_para
label_desc: Use image node_id 1 for calling a global function with example usage: get_hf_gpt_enc_layer_params(layer, config) and returns: init_ws,  init_bs