class_docstr: Base class of audio classification dataset.
class_docstr_tok: ["Base","class","of","audio","classification","dataset","."]
code: def _convert_examples_to_records(
      self, examples: List[InputExample]
  ) -> List[dict]:
      records = []
  
      for example in examples:
          record = {}
          if self.feat_type == "raw":
              record["feat"] = example.source
          elif self.feat_type == "mel":
              record["feat"] = extract_melspectrogram(
                  example.source,
                  sample_rate=self.feat_cfg["sample_rate"],
                  window_size=self.feat_cfg["window_size"],
                  hop_size=self.feat_cfg["hop_size"],
                  mel_bins=self.feat_cfg["mel_bins"],
                  fmin=self.feat_cfg["fmin"],
                  fmax=self.feat_cfg["fmax"],
                  window=self.feat_cfg["window"],
                  center=True,
                  pad_mode="reflect",
                  ref=1.0,
                  amin=1e-10,
                  top_db=None,
              )
          else:
              raise RuntimeError(
                  f"Unknown type of self.feat_type: {self.feat_type}, it must be one in {self._supported_features}"
              )
  
          record["label"] = example.label
          records.append(record)
  
      return records
  
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 3 for calling the AudioClassificationDataset obj's underlying member method code with example usage: obj._convert_examples_to_records(examples) and returns: records