code: def forward(self, src_tokens, **kwargs):
      x = self.forward_embedding(src_tokens)
  
      encoder_padding_mask = src_tokens.eq(self.padding_idx)
  
      # x: [batch_size, seq_len, hidden_size]
      for layer in self.layers:
          x = layer(x, encoder_padding_mask)
      if self.layer_norm is not None:
          x = self.layer_norm(x)
      self.batch_size = x.shape[0]
      self.beam_size = -1
  
      # B x T x C -> T x B x C
      x = x.transpose(0, 1)
  
      return EncoderOut(
          encoder_out=x,  # T x B x C
          encoder_padding_mask=encoder_padding_mask,  # B x T
          encoder_embedding=None,  # B x T x C
          encoder_states=None,
          src_tokens=None,
          src_lengths=None,
      )
  
imports: import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.modules import LayerNorm
  from lightseq.training.ops.pytorch.layer_base import TransformerEmbeddingLayerBase
  from lightseq.training.ops.pytorch.quantization import QuantLinear, disable_quant, enable_quant, ptq_mode, qat_mode
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: EncoderOut