code: def forward(self, x):
      with torch.no_grad():
          z = self.model.feature_extractor(x)
          if isinstance(z, tuple):
              z = z[0]
          c = self.model.feature_aggregator(z)
      return z, c
  
imports: import argparse
  import glob
  import os
  from shutil import copy
  import h5py
  import numpy
  import soundfile
  import torch
  import tqdm
  import fairseq
  from torch import nn
label_desc: Use image node_id 2 for calling the PretrainedWav2VecModel obj's underlying member method code with example usage: obj.forward(x) and returns: z,  c