code: def _get_spec_norms(normalized: Union[str, bool]):
      frame_length_norm, window_norm = False, False
      if torch.jit.isinstance(normalized, str):
          if normalized not in ["frame_length", "window"]:
              raise ValueError(
                  "Invalid normalized parameter: {}".format(normalized)
              )
          if normalized == "frame_length":
              frame_length_norm = True
          elif normalized == "window":
              window_norm = True
      elif torch.jit.isinstance(normalized, bool):
          if normalized:
              window_norm = True
      else:
          raise TypeError("Input type not supported")
      return frame_length_norm, window_norm
  
imports: import math
  import tempfile
  import warnings
  from collections.abc import Sequence
  from typing import List, Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torchaudio._internal.module_utils import deprecated
  from .filtering import highpass_biquad, treble_biquad
label_desc: Use image node_id 3 for calling a global function with example usage: _get_spec_norms(normalized) and returns: frame_length_norm,  window_norm