code: def forward_padding_mask(
      self,
      features: torch.Tensor,
      padding_mask: torch.Tensor,
  ) -> torch.Tensor:
      extra = padding_mask.size(1) % features.size(1)
      if extra > 0:
          padding_mask = padding_mask[:, :-extra]
      padding_mask = padding_mask.view(
          padding_mask.size(0), features.size(1), -1
      )
      padding_mask = padding_mask.all(-1)
      return padding_mask
  
imports: import logging
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from omegaconf import II
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.data.dictionary import Dictionary
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.wav2vec.wav2vec2 import EXTRACTOR_MODE_CHOICES, MASKING_DISTRIBUTION_CHOICES, LAYER_TYPE_CHOICES, ConvFeatureExtractionModel, TransformerEncoder
  from fairseq.modules import GradMultiply, LayerNorm
  from fairseq.tasks.hubert_pretraining import HubertPretrainingConfig, HubertPretrainingTask
label_desc: Use image node_id 8 for calling the HubertModel obj's underlying member method code with example usage: obj.forward_padding_mask(features, padding_mask) and returns: padding_mask