code: def forward(self, src_tokens):
      x = self.forward_embedding(src_tokens)
  
      encoder_padding_mask = src_tokens.eq(self.padding_idx)
  
      for layer in self.layers:
          x = layer(x, encoder_padding_mask)
  
      x = self.layer_norm(x)
      x = x.transpose(0, 1)
  
      return x, encoder_padding_mask
  
imports: from dataclasses import dataclass
  import torch
  import torch.nn
  from lightseq.training import LSTransformerEmbeddingLayer, LSTransformerEncoderLayer, LSTransformerDecoderLayer
  from lightseq.training.ops.pytorch.util import MODEL_ARCH
  from lightseq.training.ops.pytorch.quantization import QuantLinear
label_desc: Use image node_id 4 for calling the LSTransformerEncoder obj's underlying member method code with example usage: obj.forward(src_tokens) and returns: x,  encoder_padding_mask