code: def __init__(
      self, training_args, initial_embeddings=None, *args, **kwargs
  ):
      super(GptEmbedding, self).__init__(*args, **kwargs)
      self.emb_quant = TensorQuantizer(emb_quant_config)
  
      if initial_embeddings is not None:
          self.weight.data.copy_(
              copy_para(initial_embeddings, training_args.fp16)
          )
  
file_path: lightseq/examples/training/huggingface/gpt/ls_hf_gpt_layer.py
imports: from torch import nn
  from lightseq.training.ops.pytorch.quantization import qat_mode, enable_quant, disable_quant, QuantLinear, TensorQuantizer, emb_quant_config
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerDecoderLayer, copy_para
label_desc: Use image node_id 1 to create a new GptEmbedding object from inherited base classes: nn with example: obj = GptEmbedding(training_args, initial_embeddings)