code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      past_key_value=None,
      use_cache=False,
      *args,
      **kwargs,
  ):
      ls_encoder_padding_mask = (
          encoder_attention_mask.narrow(2, 0, 1)
          .squeeze()
          .ne(0)
          .type_as(encoder_attention_mask)
      )
  
      ls_encoder_hidden_states = encoder_hidden_states
      batch_beam = encoder_hidden_states.shape[0]
      num_beam = batch_beam // self.eval_batch_size
      if use_cache and batch_beam == hidden_states.shape[0]:
          ls_encoder_hidden_states = encoder_hidden_states[::num_beam]
          ls_encoder_padding_mask = ls_encoder_padding_mask[::num_beam]
      ls_encoder_hidden_states = ls_encoder_hidden_states.transpose(
          0, 1
      ).contiguous()
  
      cache = None
      self_attn_kv_cache = None
      if use_cache:
          cache = (
              {}
              if past_key_value is None
              else {
                  "dec_self_k": past_key_value[0],
                  "dec_self_v": past_key_value[1],
                  "encdec_kv": past_key_value[2],
              }
          )
      output = super().forward(
          hidden_states,
          ls_encoder_hidden_states,
          ls_encoder_padding_mask,
          cache,
      )
      if use_cache:
          self_attn_kv_cache = (
              cache["dec_self_k"],
              cache["dec_self_v"],
              cache["encdec_kv"] if "encdec_kv" in cache else None,
          )
      return output, self_attn_kv_cache
  
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 2 for calling the LSHFTransformerDecoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, use_cache) and returns: output,  self_attn_kv_cache