code: def forward(self, audio_path: str) -> torch.Tensor:
      waveform, sample_rate = torchaudio.load(audio_path)
      if sample_rate != 16000:
          waveform = torchaudio.functional.resample(
              waveform, float(sample_rate), 16000.0
          )
      return waveform
  
imports: import argparse
  import logging
  import os
  from typing import Tuple
  import fairseq
  import torch
  import torchaudio
  from greedy_decoder import Decoder
  from torch.utils.mobile_optimizer import optimize_for_mobile
  from torchaudio.models.wav2vec2.utils.import_fairseq import import_fairseq_model
label_desc: Use image node_id 1 for calling the Loader obj's underlying member method code with example usage: obj.forward(audio_path) and returns: waveform