code: def tik_reg(mat, reg: float = 1e-8, eps: float = 1e-8):
      """Perform Tikhonov regularization (only modifying real part).
  
      Args:
          mat (torch.complex64/ComplexTensor): input matrix (..., C, C)
          reg (float): regularization factor
          eps (float)
      Returns:
          ret (torch.complex64/ComplexTensor): regularized matrix (..., C, C)
      """
      # Add eps
      C = mat.size(-1)
      eye = torch.eye(C, dtype=mat.dtype, device=mat.device)
      shape = [1 for _ in range(mat.dim() - 2)] + [C, C]
      eye = eye.view(*shape).repeat(*mat.shape[:-2], 1, 1)
      with torch.no_grad():
          epsilon = FC.trace(mat).real[..., None, None] * reg
          # in case that correlation_matrix is all-zero
          epsilon = epsilon + eps
      mat = mat + epsilon * eye
      return mat
  
imports: from typing import List, Union
  import torch
  from packaging.version import parse
  from torch_complex import functional
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import cat, complex_norm, einsum, inverse, is_complex, is_torch_complex_tensor, matmul, reverse, solve, to_double
label_desc: Use image node_id 22 for calling a global function with example usage: tik_reg(mat, reg, eps) and returns: mat