code: def high_pass_filter(
      audio: Union[str, np.ndarray],
      sample_rate: int = DEFAULT_SAMPLE_RATE,
      cutoff_hz: float = 3000.0,
      output_path: Optional[str] = None,
      metadata: Optional[List[Dict[str, Any]]] = None,
  ) -> Tuple[np.ndarray, int]:
      """
      Allows audio signals with a frequency higher than the given cutoff to pass
      through and attenuates signals with frequencies lower than the cutoff frequency
  
      @param audio: the path to the audio or a variable of type np.ndarray that
          will be augmented
  
      @param sample_rate: the audio sample rate of the inputted audio
  
      @param cutoff_hz: frequency (in Hz) where signals with lower frequencies will
          begin to be reduced by 6dB per octave (doubling in frequency) below this point
  
      @param output_path: the path in which the resulting audio will be stored. If None,
          the resulting np.ndarray will still be returned
  
      @param metadata: if set to be a list, metadata about the function execution
          including its name, the source & dest duration, sample rates, etc. will be
          appended to the inputted list. If set to None, no metadata will be appended
  
      @returns: the augmented audio array and sample rate
      """
      assert isinstance(
          cutoff_hz, (int, float)
      ), "Expected 'cutoff_hz' to be a number"
      audio, sample_rate = audutils.validate_and_load_audio(
          audio, sample_rate
      )
  
      if metadata is not None:
          func_kwargs = deepcopy(locals())
          func_kwargs.pop("metadata")
  
      num_channels = 1 if audio.ndim == 1 else audio.shape[0]
      audio = audio.reshape((num_channels, -1))
  
      aug_audio, out_sample_rate = sox_effects.apply_effects_tensor(
          torch.Tensor(audio),
          sample_rate,
          [["highpass", str(cutoff_hz)]],
      )
      high_pass_array = aug_audio.numpy()
  
      if metadata is not None:
          audutils.get_metadata(
              metadata=metadata,
              function_name="high_pass_filter",
              dst_audio=high_pass_array,
              dst_sample_rate=out_sample_rate,
              # pyre-fixme[61]: `func_kwargs` may not be initialized here.
              **func_kwargs,
          )
  
      return audutils.ret_and_save_audio(
          high_pass_array, output_path, out_sample_rate
      )
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from augly.audio import utils
  from augly.utils import DEFAULT_SAMPLE_RATE
  from augly.utils.libsndfile import install_libsndfile
  import librosa
  from torchaudio import sox_effects
  from torchaudio.functional import fftconvolve, resample
label_desc: Use image node_id 7 for calling a global function with example usage: high_pass_filter(audio, sample_rate, cutoff_hz, output_path, metadata) and returns: audutils