code: def _assert_psd_matrices(
      psd_s: torch.Tensor, psd_n: torch.Tensor
  ) -> None:
      """Assertion checks of the PSD matrices of target speech and noise.
  
      Args:
          psd_s (torch.Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
              Tensor with dimensions `(..., freq, channel, channel)`.
          psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
              Tensor with dimensions `(..., freq, channel, channel)`.
      """
      if psd_s.ndim < 3 or psd_n.ndim < 3:
          raise ValueError(
              "Expected at least 3D Tensor (..., freq, channel, channel) for psd_s and psd_n. "
              f"Found {psd_s.shape} for psd_s and {psd_n.shape} for psd_n."
          )
      if not (psd_s.is_complex() and psd_n.is_complex()):
          raise TypeError(
              "The type of psd_s and psd_n must be ``torch.cfloat`` or ``torch.cdouble``. "
              f"Found {psd_s.dtype} for psd_s and {psd_n.dtype} for psd_n."
          )
      if psd_s.shape != psd_n.shape:
          raise ValueError(
              f"The dimensions of psd_s and psd_n should be the same. Found {psd_s.shape} and {psd_n.shape}."
          )
      if psd_s.shape[-1] != psd_s.shape[-2]:
          raise ValueError(
              f"The last two dimensions of psd_s should be the same. Found {psd_s.shape}."
          )
  
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 41 for calling a global function with example usage: _assert_psd_matrices(psd_s, psd_n) without return types