code: def forward(
      self,
      src_tokens: torch.Tensor,
      dur_src: torch.Tensor,
      f0_src: torch.Tensor,
      src_lengths: Optional[Any] = None,
      incremental_state: Optional[
          Dict[str, Dict[str, Optional[Tensor]]]
      ] = None,
  ):
      if self.ignore_duration_input:
          dur_src = torch.zeros_like(dur_src)
  
      if self.ignore_f0_input:
          f0_src = torch.zeros_like(f0_src)
  
      if self.training:
          (
              src_tokens,
              unit_mask,
              dur_src,
              dur_mask,
              f0_src,
              f0_mask,
          ) = self.apply_dropout(src_tokens, dur_src, f0_src)
      else:
          unit_masks = dur_mask = f0_mask = None
  
      prediction, _ = self.decoder(
          prev_output_tokens=(src_tokens, dur_src, f0_src),
          incremental_state=incremental_state,
          src_lengths=src_lengths,
          features_only=True,
      )
  
      result = dict(zip(self.channel_names, prediction))
  
      return result
  
imports: from dataclasses import dataclass, field
  from fairseq.models.fairseq_decoder import FairseqDecoder
  import numpy
  from typing import Optional, Dict, Any, List
  import torch
  from torch import nn
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum
  from fairseq.models import FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.tasks.speech_ulm_task import SpeechUnitLanguageModelingTask
  from fairseq.models.transformer import Embedding, TransformerDecoder, Linear
  from fairseq.models.transformer_lm import TransformerLanguageModelConfig
  from torch import Tensor
label_desc: Use image node_id 6 for calling the TransformerUnitLanguageModel obj's underlying member method code with example usage: obj.forward(src_tokens, dur_src, f0_src, src_lengths, incremental_state) and returns: result