code: def build_model(cls, config, params_list, layer_list, layer_id):
      layer = layer_list[layer_id]
      modules_list = []
      # only python >= 3.6 (orderedDict)
      for module_name in params_list.__dict__.values():
          exec(f"modules_list.append(layer.{module_name})")
      init_ws = []
      init_bs = []
      for module in modules_list:
          w, b = get_weight_and_bias(module)
          init_ws.append(w)
          init_bs.append(b)
      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 cls(config, init_ws, init_bs)
  
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 4 for calling the LSHFTransformerEncoderLayer obj's underlying member method code with example usage: obj.build_model(cls, config, params_list, layer_list, layer_id) and returns: cls