code: def _get_waveform_and_window_properties(
      waveform: Tensor,
      channel: int,
      sample_frequency: float,
      frame_shift: float,
      frame_length: float,
      round_to_power_of_two: bool,
      preemphasis_coefficient: float,
  ) -> Tuple[Tensor, int, int, int]:
      r"""Gets the waveform and window properties"""
      channel = max(channel, 0)
      assert channel < waveform.size(
          0
      ), "Invalid channel {} for size {}".format(
          channel, waveform.size(0)
      )
      waveform = waveform[channel, :]  # size (n)
      window_shift = int(
          sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS
      )
      window_size = int(
          sample_frequency * frame_length * MILLISECONDS_TO_SECONDS
      )
      padded_window_size = (
          _next_power_of_2(window_size)
          if round_to_power_of_two
          else window_size
      )
  
      assert (
          2 <= window_size <= len(waveform)
      ), "choose a window size {} that is [2, {}]".format(
          window_size, len(waveform)
      )
      assert 0 < window_shift, "`window_shift` must be greater than 0"
      assert padded_window_size % 2 == 0, (
          "the padded `window_size` must be divisible by two."
          " use `round_to_power_of_two` or change `frame_length`"
      )
      assert (
          0.0 <= preemphasis_coefficient <= 1.0
      ), "`preemphasis_coefficient` must be between [0,1]"
      assert (
          sample_frequency > 0
      ), "`sample_frequency` must be greater than zero"
      return waveform, window_shift, window_size, padded_window_size
  
imports: import math
  from typing import Tuple
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 6 for calling a global function with example usage: _get_waveform_and_window_properties(waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient) and returns: waveform,  window_shift,  window_size,  padded_window_size