code: def _short_time_fourier_transform(
      data: torch.Tensor,
      sampling_rate_in_hz: int,
      window_length_in_s: float,
      window_shift_in_s: float,
      num_fft_points: int,
      window_type: str,
      data_transformation: Optional[str] = None,
      zero_mean_offset: bool = False,
  ) -> torch.Tensor:
      window_length_in_samp: int = get_length_in_samp(
          window_length_in_s, sampling_rate_in_hz
      )
      window_shift_in_samp: int = get_length_in_samp(
          window_shift_in_s, sampling_rate_in_hz
      )
      preprocessed_data_matrix = _preprocess_to_padded_matrix(
          data[0],
          window_length_in_samp,
          window_shift_in_samp,
          zero_mean_offset=zero_mean_offset,
      )
      weighted_data_matrix = _weight_data_matrix(
          preprocessed_data_matrix,
          window_type,
          data_transformation=data_transformation,
      )
      fft = torch.fft.fft(weighted_data_matrix, n=num_fft_points)
      return fft
  
imports: import functools
  import logging
  from io import BytesIO
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn.functional
  import torchaudio
  from packaging import version
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import DEFAULT_AUDIO_TENSOR_LENGTH
  from ludwig.utils.types import TorchAudioTuple
label_desc: Use image node_id 16 for calling a global function with example usage: _short_time_fourier_transform(data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation, zero_mean_offset) and returns: fft