code: def forward(
      self, hidden_states, attention_mask=None, *args, **kwargs
  ):
      if attention_mask is not None:
          ls_attention_mask = attention_mask.squeeze()
      else:
          ls_attention_mask = torch.zeros(hidden_states.size()[:2])
      output = super().forward(hidden_states, ls_attention_mask)
      return output
  
imports: import time
  import torch
  from torch import nn
  from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode, QuantLinear, TensorQuantizer, weight_quant_config, emb_quant_config
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerDecoderLayer
  from export.util import parse_args
label_desc: Use image node_id 2 for calling the LSHFGptEncoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask) and returns: output