code: def forward(
      self,
      x,
      self_attn_mask=None,
      self_attn_padding_mask=None,
      need_weights=None,
      att_args=None,
  ):
      x, attn = super().forward(
          x,
          self_attn_mask,
          self_attn_padding_mask,
          need_weights,
          att_args,
      )
      return x
  
imports: import copy
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import register_model, register_model_architecture, FairseqEncoder
  from fairseq.models.speech_to_text import Wav2VecEncoderWithAdaptor
  from fairseq.models.speech_to_text.xm_transformer import set_default_adaptor_args, set_default_w2v_encoder_args, need_finetuning
  from fairseq.models.transformer import TransformerEncoder, TransformerDecoder
  from fairseq.models.wav2vec import TransformerSentenceEncoderLayer
  from fairseq.utils import safe_hasattr
  from .s2t_dualinputtransformer import DualInputS2TTransformerModel, TransformerMultiInputDecoder, DualInputEncoder
label_desc: Use image node_id 2 for calling the TransformerSentenceEncoderLayerStd obj's underlying member method code with example usage: obj.forward(x, _attn_mask, _attn_padding_mask, need_weights, att_args) and returns: x