How can I retrieve the value at which the error occured in Python?

Question

I'm using a package in Python and it often throws up an error. The error is from the Cholesky decomposition (specifically from the function torch.linalg_cholesky). By the time the error arises, my input data to the original function has gone through several steps, so that I don't have immediate access to the input to torch.linalg_cholesky, without performing those many intermediate steps myself. Is there a way I can retrieve the values that were used as an input to the torch.linalg_cholesky function?

More details: I'm using the MOGPTK package in Python. As part of this, I run the function

model_BNSE.init_parameters('BNSE')

Often I get the following error:

  File "C:\Users\simon\AppData\Local\Temp\ipykernel_37540\2931476796.py", line 1, in <module>
    model_BNSE.init_parameters('BNSE')

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\models\mosm.py", line 85, in init_parameters
    amplitudes, means, variances = self.dataset.get_bnse_estimation(self.Q)

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\dataset.py", line 615, in get_bnse_estimation
    channel_amplitudes, channel_means, channel_variances = channel.get_bnse_estimation(Q, n)

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\data.py", line 955, in get_bnse_estimation
    w, psd = BNSE(x[:,i], y, max_freq=nyquist[i], n=n)

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\bnse.py", line 35, in BNSE
    loss = optimizer.step(model.loss)

  File "C:\Users\simon\Anaconda3\envs\myenv\lib\site-packages\torch\optim\optimizer.py", line 88, in wrapper
    return func(*args, **kwargs)

  File "C:\Users\simon\Anaconda3\envs\myenv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)

  File "C:\Users\simon\Anaconda3\envs\myenv\lib\site-packages\torch\optim\lbfgs.py", line 437, in step
    loss = float(closure())

  File "C:\Users\simon\Anaconda3\envs\myenv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\gpr\model.py", line 219, in loss
    loss = -self.log_marginal_likelihood() - self.log_prior()

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\gpr\model.py", line 277, in log_marginal_likelihood
    L = self._cholesky(Kff, add_jitter=self.variance_per_data)  # NxN

  File "c:\users\simon\documents\project\python packages\mogptk\mogptk\gpr\model.py", line 209, in _cholesky
    raise CholeskyException(e.args[0], K, self)

CholeskyException: torch.linalg_cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 13 is not positive-definite).`

I would like to be able to retrieve the matrix Kff that went into the function at the point the error occured.