code: def forward(
      self, hidden_states, encoder_padding_mask, *args, **kwargs
  ):
      ls_encoder_padding_mask = (
          encoder_padding_mask.narrow(2, 0, 1)
          .squeeze()
          .ne(0)
          .type_as(encoder_padding_mask)
      )
      output = super().forward(hidden_states, ls_encoder_padding_mask)
      return (output, None, None, None)
  
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 LSHFTransformerEncoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states, encoder_padding_mask) and returns: output,  None,  None,  None