code: def get_hf_bert_enc_layer_params(layer):
      init_ws = []
      init_bs = []
  
      init_ws.append(layer.attention.self.query.weight.detach().clone())
      init_bs.append(layer.attention.self.query.bias.detach().clone())
      init_ws.append(layer.attention.self.key.weight.detach().clone())
      init_bs.append(layer.attention.self.key.bias.detach().clone())
      init_ws.append(layer.attention.self.value.weight.detach().clone())
      init_bs.append(layer.attention.self.value.bias.detach().clone())
      init_ws.append(
          layer.attention.output.dense.weight.detach().clone()
      )
      init_bs.append(layer.attention.output.dense.bias.detach().clone())
      init_ws.append(
          layer.attention.output.LayerNorm.weight.detach().clone()
      )
      init_bs.append(
          layer.attention.output.LayerNorm.bias.detach().clone()
      )
  
      init_ws.append(layer.intermediate.dense.weight.detach().clone())
      init_bs.append(layer.intermediate.dense.bias.detach().clone())
      init_ws.append(layer.output.dense.weight.detach().clone())
      init_bs.append(layer.output.dense.bias.detach().clone())
      init_ws.append(layer.output.LayerNorm.weight.detach().clone())
      init_bs.append(layer.output.LayerNorm.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
  
imports: import torch
  from lightseq.training.pytorch_quantization.nn.modules.tensor_quantizer import enable_quant
  from lightseq.training.ops.pytorch.quantization import qat_mode, disable_quant, weight_quant_config, act_quant_config, relu_quant_config
  from lightseq.training.ops.pytorch.torch_transformer_layers import BertEmbeddingLayer
  from transformers import BertForSequenceClassification, BertPreTrainedModel, BertLayer, BertLMHeadModel, BertForMaskedLM, BertForNextSentencePrediction, BertForMultipleChoice, BertForTokenClassification, BertForQuestionAnswering
label_desc: Use image node_id 1 for calling a global function with example usage: get_hf_bert_enc_layer_params(layer) and returns: init_ws,  init_bs