code: def _create_triangular_filter(
      start_bin_freq: torch.Tensor,
      middle_bin_freq: torch.Tensor,
      end_bin_freq: torch.Tensor,
      num_ess_fft_points: int,
  ):
      filter_window = torch.zeros(
          num_ess_fft_points,
          dtype=torch.float32,
          device=start_bin_freq.device,
      )
      filt_support_begin = middle_bin_freq - start_bin_freq
      filt_support_end = end_bin_freq - middle_bin_freq
      for freq in range(int(start_bin_freq), int(middle_bin_freq)):
          filter_window[freq] = (
              freq - start_bin_freq
          ) / filt_support_begin
      for freq in range(int(middle_bin_freq), int(end_bin_freq)):
          filter_window[freq] = (end_bin_freq - freq) / filt_support_end
      return filter_window
  
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 12 for calling a global function with example usage: _create_triangular_filter(start_bin_freq, middle_bin_freq, end_bin_freq, num_ess_fft_points) and returns: filter_window