docs: def fully_correlated_conditional(Kmn, Kmm, Knn, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False):
      """
      This function handles conditioning of multi-output GPs in the case where the conditioning
      points are all fully correlated, in both the prior and posterior.
      :param Kmn: LM x N x P
      :param Kmm: LM x LM
      :param Knn: N x P or N x P x N x P
      :param f: data matrix, LM x 1
      :param q_sqrt: 1 x LM x LM  or 1 x ML
      :param full_cov: calculate covariance between inputs
      :param full_output_cov: calculate covariance between outputs
      :param white: use whitened representation
      :return:
          - mean: N x P
          - variance: N x P, N x P x P, P x N x N, N x P x N x P
      """
      m, v = fully_correlated_conditional_repeat(Kmn, Kmm, Knn, f, full_cov=full_cov,
                                                 full_output_cov=full_output_cov, q_sqrt=q_sqrt, white=white)
      return m[0, ...], v[0, ...]