code: def apply_seg_dropout(
      self, inp, mask_prob, mask_leng, mask_type, mask_val
  ):
      B, T = inp.size()
      if mask_prob > 0:
          mask_indices = compute_mask_indices(
              (B, T),
              None,
              mask_prob,
              mask_leng,
              mask_type,  # may mask padding
          )
          mask_indices = torch.from_numpy(mask_indices).to(inp.device)
          inp[mask_indices] = mask_val
      else:
          mask_indices = torch.zeros_like(inp).bool()
      return inp, mask_indices
  
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 3 for calling the TransformerUnitLanguageModel obj's underlying member method code with example usage: obj.apply_seg_dropout(inp, mask_prob, mask_leng, mask_type, mask_val) and returns: inp,  mask_indices