code: def forward(self, src_tokens, *args, **kwargs):
      encoder_out = super().forward(src_tokens, *args, **kwargs)
  
      x = encoder_out["encoder_out"][0]  # T x B x C
      padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1)
  
      if padding_mask.any():
          x = (
              x.float()
              .masked_fill_(padding_mask, float("-inf"))
              .type_as(x)
          )
  
      # Build the sentence embedding by max-pooling over the encoder outputs
      sentemb = x.max(dim=0)[0]
  
      # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
      # `foward` so we use a dictionary instead.
      # TorchScript does not support mixed values so the values are all lists.
      # The empty list is equivalent to None.
      return {"sentemb": [sentemb]}
  
imports: import logging
  from typing import Any, Dict, List, Optional
  from torch import Tensor
  import torch
  import torch.nn
  from fairseq.models import FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import base_architecture, Embedding, TransformerModel, TransformerEncoder, TransformerDecoder
  from fairseq.modules import TransformerDecoderLayer
label_desc: Use image node_id 2 for calling the LaserTransformerEncoder obj's underlying member method code with example usage: obj.forward(src_tokens) and returns: dict