docstr: Tests filtering signals with frequency sampling method.
  
  Generate random signals and magnitudes and filter using fft_convolve().
  
  Args:
    n_frequencies: Number of magnitudes.
    n_frames: Number of frames for a time-varying filter.
    window_size: Size of window for generating impulse responses.
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code: def test_frequency_filter_gives_correct_size(
      self, n_frequencies, n_frames, window_size
  ):
      """Tests filtering signals with frequency sampling method.
  
      Generate random signals and magnitudes and filter using fft_convolve().
  
      Args:
        n_frequencies: Number of magnitudes.
        n_frames: Number of frames for a time-varying filter.
        window_size: Size of window for generating impulse responses.
      """
      # Create transfer function.
      if n_frames > 0:
          magnitudes = np.random.uniform(
              size=(1, n_frames, n_frequencies)
          ).astype(np.float32)
      else:
          magnitudes = np.random.uniform(
              size=(1, n_frequencies)
          ).astype(np.float32)
  
      audio_out = core.frequency_filter(
          self.audio,
          magnitudes,
          window_size=window_size,
          padding="same",
      )
  
      audio_out_size = int(audio_out.shape[-1])
      self.assertEqual(audio_out_size, self.audio_size)
  
imports: from absl.testing import parameterized
  from ddsp import core
  import librosa
  import numpy
  from scipy import signal
  import tensorflow.compat.v2
label_desc: Use image node_id 9 for calling the FiniteImpulseResponseTest obj's underlying member method code with example usage: obj.test_frequency_filter_gives_correct_size(n_frequencies, n_frames, window_size) without return types