class_docstr: The base class for audio classifier models.
code: def predict(
      self,
      data: List[List[float]],
      sample_rate: int,
      batch_size: int = 1,
      feat_type: str = "mel",
      use_gpu: bool = False,
  ):
      if (
          self.task not in self._tasks_supported
          and self.task is not None
      ):  # None for getting audioset tags
          raise RuntimeError(
              f"Unknown task {self.task}, current tasks supported:\n"
              "1. sound-cls: sound classification;\n"
              "2. None: audioset tags"
          )
  
      paddle.set_device("gpu") if use_gpu else paddle.set_device("cpu")
  
      batches = self._batchify(data, sample_rate, feat_type, batch_size)
      results = []
      self.eval()
      for batch in batches:
          feats = paddle.to_tensor(batch)
          scores = self(feats)
  
          for score in scores.numpy():
              result = OrderedDict()
              for i in (-score).argsort():
                  result[self.label_map[i]] = score[i]
              results.append(result)
  
      return results
  
file_path: PaddleHub/paddlehub/module/audio_module.py
imports: from collections import OrderedDict
  from typing import List, Tuple
  import numpy
  import paddle
  from paddlehub.module.module import RunModule, runnable, serving
  from paddlehub.utils.utils import extract_melspectrogram
label_desc: Use image node_id 4 for calling the AudioClassifierModule obj's underlying member method code with example usage: obj.predict(data, sample_rate, batch_size, feat_type, use_gpu) and returns: results