code: def forward(
      self, hidden_states, encoder_padding_mask, *args, **kwargs
  ):
      ls_encoder_padding_mask = encoder_padding_mask / -10000.0
      ls_encoder_padding_mask = ls_encoder_padding_mask.squeeze()
      output = super().forward(hidden_states, ls_encoder_padding_mask)
      return (output, None, None, None)
  
imports: import time
  import torch
  from transformers import BertTokenizer, BertForTokenClassification, BertConfig
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode
  from lightseq.training.ops.pytorch.torch_transformer_layers import BertEmbeddingLayer, TransformerEncoderLayer
  from export.util import parse_args
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