code: def get_group_delay(
      raw_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,
  ):
      X_stft_transform = _get_stft(
          raw_data,
          sampling_rate_in_hz,
          window_length_in_s,
          window_shift_in_s,
          num_fft_points,
          window_type=window_type,
      )
      Y_stft_transform = _get_stft(
          raw_data,
          sampling_rate_in_hz,
          window_length_in_s,
          window_shift_in_s,
          num_fft_points,
          window_type=window_type,
          data_transformation="group_delay",
      )
      X_stft_transform_real = torch.real(X_stft_transform)
      X_stft_transform_imag = torch.imag(X_stft_transform)
      Y_stft_transform_real = torch.real(Y_stft_transform)
      Y_stft_transform_imag = torch.imag(Y_stft_transform)
      nominator = torch.multiply(
          X_stft_transform_real, Y_stft_transform_real
      ) + torch.multiply(X_stft_transform_imag, Y_stft_transform_imag)
      denominator = torch.square(torch.abs(X_stft_transform))
      group_delay = torch.divide(nominator, denominator + 1e-10)
      assert not torch.isnan(
          group_delay
      ).any(), "There are NaN values in group delay"
      return torch.transpose(group_delay, 0, 1)
  
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 7 for calling a global function with example usage: get_group_delay(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type) and returns: torch