code: def __init__(self, config):
      super(LSTransformer, self).__init__()
      self.config = config
  
      print("Lightseq Transformer config is ", self.config.__dict__)
  
      if self.config.local_rank >= 0:
          torch.cuda.set_device(self.config.local_rank)
  
      self.build_model(self.config)
  
imports: from dataclasses import dataclass
  import torch
  import torch.nn
  from lightseq.training import LSTransformerEmbeddingLayer, LSTransformerEncoderLayer, LSTransformerDecoderLayer
  from lightseq.training.ops.pytorch.util import MODEL_ARCH
  from lightseq.training.ops.pytorch.quantization import QuantLinear
label_desc: Use image node_id 1 to create a new LSTransformer object from inherited base classes: nn with example: obj = LSTransformer(config)