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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _conditional(Xnew, feat, kern, f, *, full_cov=False, full_output_cov=False, q_sqrt=None, white=False):
 """ Most efficient routine to project L independent latent gps through a mixing matrix W. The mixing matrix is a member of the `SeparateMixedMok` and has shape P x L. The covariance matrices used to calculate the conditional have the following shape: - Kuu: L x M x M - Kuf: L x M x N - Kff: L x N or L x N x N Further reference - See `gpflow.conditionals._conditional` for a detailed explanation of conditional in the single-output case. - See the multiouput notebook for more information about the multiouput framework. """

    logger.debug("conditional: (MixedKernelSharedMof, MixedKernelSeparateMof), SeparateMixedMok")
    with params_as_tensors_for(feat, kern):
        independent_cond = conditional.dispatch(object, SeparateIndependentMof, SeparateIndependentMok, object)
        gmu, gvar = independent_cond(Xnew, feat, kern, f, full_cov=full_cov, q_sqrt=q_sqrt,
                                    full_output_cov=False, white=white)  # N x L, L x N x N or N x L
        return _mix_latent_gp(kern.W, gmu, gvar, full_cov, full_output_cov)