code: def __init__(self, eval_batch_size, *args, **kwargs):
      super(LSHFTransformerDecoderLayer, self).__init__(*args, **kwargs)
      self.eval_batch_size = eval_batch_size
  
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 to create a new LSHFTransformerDecoderLayer object from inherited base classes: TransformerDecoderLayer with example: obj = LSHFTransformerDecoderLayer(eval_batch_size)