code: def from_pretrained(self, *args, training_args, **kwargs):
      self.config = kwargs["config"]
      model = super().from_pretrained(*args, **kwargs)
      inject_lightseq_layer(model, training_args, self.config)
      return model
  
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 1 for calling the LSBartPretrainedModel obj's underlying member method code with example usage: obj.from_pretrained() and returns: model