code: def _preprocess_to_padded_matrix(
      data: torch.Tensor,
      window_length_in_samp: int,
      window_shift_in_samp: int,
      zero_mean_offset: bool = False,
  ) -> torch.Tensor:
      num_input = data.shape[0]
      num_output = get_num_output_padded_to_fit_input(
          num_input, window_length_in_samp, window_shift_in_samp
      )
      zero_padded_matrix = torch.zeros(
          (num_output, window_length_in_samp),
          dtype=torch.float32,
          device=data.device,
      )
      for num_output_idx in range(num_output):
          start_idx = window_shift_in_samp * num_output_idx
          is_last_output = num_output_idx == num_output - 1
          end_idx = (
              start_idx + window_length_in_samp
              if not is_last_output
              else num_input
          )
          end_padded_idx = (
              window_length_in_samp
              if not is_last_output
              else end_idx - start_idx
          )
          window_data = data[start_idx:end_idx]
          if zero_mean_offset:
              window_data = window_data - torch.mean(window_data)
          zero_padded_matrix[
              num_output_idx, :end_padded_idx
          ] = window_data
      return zero_padded_matrix
  
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 17 for calling a global function with example usage: _preprocess_to_padded_matrix(data, window_length_in_samp, window_shift_in_samp, zero_mean_offset) and returns: zero_padded_matrix