code: def apply_mask(
      self,
      x,
      padding_mask,
      mask_indices=None,
      mask_channel_indices=None,
  ):
      B, T, C = x.shape
  
      if self.mask_channel_prob > 0 and self.mask_channel_before:
          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
  
      if self.mask_prob > 0:
          if mask_indices is None:
              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,
                  require_same_masks=self.cfg.require_same_masks,
                  mask_dropout=self.cfg.mask_dropout,
              )
              mask_indices = torch.from_numpy(mask_indices).to(x.device)
          x = index_put(x, mask_indices, self.mask_emb)
      else:
          mask_indices = None
  
      if self.mask_channel_prob > 0 and not self.mask_channel_before:
          if mask_channel_indices is None:
              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 = index_put(x, mask_channel_indices, 0)
  
      return x, mask_indices
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 4 for calling the Wav2Vec2Model obj's underlying member method code with example usage: obj.apply_mask(x, padding_mask, mask_indices, mask_channel_indices) and returns: x,  mask_indices