code: def forward(self, hidden_states, *args, **kwargs):
      ls_encoder_padding_mask = torch.zeros(
          hidden_states.size()[:-1]
      ).to(hidden_states.device)
      output = super().forward(hidden_states, ls_encoder_padding_mask)
      return (output,)
  
imports: import time
  import requests
  from PIL import Image
  import torch
  from transformers import ViTFeatureExtractor, ViTForImageClassification, ViTConfig
  import lightseq.inference
  from lightseq.training.ops.pytorch.quantization import qat_mode
  from lightseq.training.ops.pytorch.torch_transformer_layers import TransformerEncoderLayer
  from export.util import parse_args
label_desc: Use image node_id 2 for calling the LSVITTransformerEncoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: output