code: def test_audio_input_feature(encoder: str) -> None:
      audio_config = {
          "name": "audio_feature",
          "type": "audio",
          "preprocessing": {
              "type": "fbank",
              "window_length_in_s": 0.04,
              "window_shift_in_s": 0.02,
              "num_filter_bands": 80,
              "audio_file_length_limit_in_s": 3.0,
          },
          "encoder": {
              "type": encoder,
              "should_embed": False,
              "vocab": VOCAB,
              "max_sequence_length": SEQ_SIZE,
              "embedding_size": AUDIO_W_SIZE,
          },
      }
  
      audio_config, _ = load_config_with_kwargs(
          AudioInputFeatureConfig, audio_config
      )
      audio_input_feature = AudioInputFeature(audio_config)
  
      audio_tensor = torch.randn(
          [BATCH_SIZE, SEQ_SIZE, AUDIO_W_SIZE], dtype=torch.float32
      ).to(DEVICE)
      encoder_output = audio_input_feature(audio_tensor)
      assert (
          encoder_output[ENCODER_OUTPUT].shape[1:]
          == audio_input_feature.output_shape
      )
  
file_path: ludwig/tests/ludwig/features/test_audio_feature.py
imports: import os
  from random import choice
  from string import ascii_lowercase, ascii_uppercase, digits
  import pandas
  import pytest
  import torch
  from ludwig.backend import LOCAL_BACKEND
  from ludwig.constants import BFILL, ENCODER_OUTPUT, PROC_COLUMN
  from ludwig.features.audio_feature import AudioFeatureMixin, AudioInputFeature
  from ludwig.schema.features.audio_feature import AudioInputFeatureConfig
  from ludwig.schema.utils import load_config_with_kwargs
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.utils import audio_feature, category_feature, generate_data
label_desc: Use image node_id 1 for calling a global function with example usage: test_audio_input_feature(encoder) without return types