code: def extract_features(
      self,
      source: torch.Tensor,
      padding_mask: Optional[torch.Tensor] = None,
      mask: bool = False,
      ret_conv: bool = False,
      output_layer: Optional[int] = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      res = self.forward(
          source,
          padding_mask=padding_mask,
          mask=mask,
          features_only=True,
          output_layer=output_layer,
      )
      feature = res["features"] if ret_conv else res["x"]
      return feature, res["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 10 for calling the HubertModel obj's underlying member method code with example usage: obj.extract_features(source, padding_mask, mask, ret_conv, output_layer) and returns: feature,  res