code: def apply_mask(self, x, padding_mask):
      B, T, C = x.shape
      if self.mask_prob > 0:
          mask_indices = compute_mask_indices(
              (B, T),
              padding_mask,
              self.mask_prob,
              self.mask_length,
              self.mask_selection,
              self.mask_other,
              min_masks=2,
              no_overlap=self.no_mask_overlap,
              min_space=self.mask_min_space,
          )
          mask_indices = torch.from_numpy(mask_indices).to(x.device)
          x[mask_indices] = self.mask_emb
      else:
          mask_indices = None
  
      if self.mask_channel_prob > 0:
          mask_channel_indices = compute_mask_indices(
              (B, C),
              None,
              self.mask_channel_prob,
              self.mask_channel_length,
              self.mask_channel_selection,
              self.mask_channel_other,
              no_overlap=self.no_mask_channel_overlap,
              min_space=self.mask_channel_min_space,
          )
          mask_channel_indices = (
              torch.from_numpy(mask_channel_indices)
              .to(x.device)
              .unsqueeze(1)
              .expand(-1, T, -1)
          )
          x[mask_channel_indices] = 0
  
      return x, mask_indices
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from TTS.vc.modules.freevc.wavlm.modules import Fp32GroupNorm, Fp32LayerNorm, GLU_Linear, GradMultiply, MultiheadAttention, SamePad, TransposeLast, get_activation_fn, init_bert_params
label_desc: Use image node_id 2 for calling the WavLM obj's underlying member method code with example usage: obj.apply_mask(x, padding_mask) and returns: x,  mask_indices