code: def get_hf_bert_emb_layer_params(layer):
      init_ws = []
  
      init_ws.append(layer.word_embeddings.weight.detach().clone())
      init_ws.append(layer.position_embeddings.weight.detach().clone())
      init_ws.append(
          layer.token_type_embeddings.weight.detach().clone()
      )
      init_ws.append(layer.LayerNorm.weight.detach().clone())
      init_ws.append(layer.LayerNorm.bias.detach().clone())
  
      return init_ws
  
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 2 for calling a global function with example usage: get_hf_bert_emb_layer_params(layer) and returns: init_ws