class_docstr: Base class of audio classification dataset.
class_docstr_tok: ["Base","class","of","audio","classification","dataset","."]
code: def _read_file(self, input_file: str) -> List[InputExample]:
      if not os.path.exists(input_file):
          raise RuntimeError(
              "Data file: {} not found.".format(input_file)
          )
  
      examples = []
      if self.file_type == "npz":
          dataset = np.load(
              os.path.join(self.data_file), allow_pickle=True
          )
          audio_id = 0
          for waveform, label in zip(
              dataset["waveforms"], dataset["labels"]
          ):
              example = InputExample(
                  guid=audio_id, source=waveform, label=label
              )
              audio_id += 1
              examples.append(example)
      else:
          raise NotImplementedError(
              f"Only soppurts npz file type, but got {self.file_type}"
          )
  
      return examples
  
imports: import csv
  import io
  import os
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import paddle
  from paddlehub.utils.utils import extract_melspectrogram
label_desc: Use image node_id 2 for calling the AudioClassificationDataset obj's underlying member method code with example usage: obj._read_file(input_file) and returns: examples