docstr: Configuration of model hyperparameters for encoder and decoder
docstr_tok: ["Configuration","of","model","hyperparameters","for","encoder","and","decoder"]
code: def get_params_list(**kwargs):
      """Configuration of model hyperparameters for encoder and decoder"""
  
      @dataclass
      class ParamsList:
          self_attn_q_proj: None
          self_attn_k_proj: None
          self_attn_v_proj: None
          self_attn_out_proj: None
          self_attn_layer_norm: None
          fc1: None
          fc2: None
          final_layer_norm: None
  
      params_list = ParamsList(**kwargs)
      return params_list
  
imports: import torch
  from torch import nn
  from dataclasses import dataclass
  from lightseq.training.ops.pytorch.transformer_encoder_layer import LSTransformerEncoderLayer
  from lightseq.training.ops.pytorch.transformer_decoder_layer import LSTransformerDecoderLayer
  from lightseq.training.ops.pytorch.quantization import weight_quant_config, act_quant_config, relu_quant_config
  from transformers import BartPretrainedModel, BartForConditionalGeneration, BartForSequenceClassification, BartForQuestionAnswering
  from transformers.models.bart.modeling_bart import BartEncoderLayer, BartDecoderLayer
label_desc: Use image node_id 3 for calling the LSHFTransformerEncoderLayer obj's underlying member method code with example usage: obj.get_params_list() and returns: params_list