from gpflow.conditionals import conditional
from gpflow.inducing_variables import SeparateIndependentInducingVariables
from gpflow.kernels import SeparateIndependent

#note: object 'm' is of type gpflow.models.svgp.SVGP

ind_conditional = conditional.dispatch(
    object, SeparateIndependentInducingVariables, SeparateIndependent, object)
gmu, gvar = ind_conditional(
    X,
    m.inducing_variable,
    m.kernel,
    m.q_mu,
    full_cov=False,
    q_sqrt=m.q_sqrt,
    full_output_cov=False,
    white=False
)
# extract fitted parameters
Wfit = m.kernel.W.numpy()
fm,fv = m.predict_f(X)
fm = fm.numpy()
fm2 = gmu.numpy()@Wfit.T
np.max(np.abs(fm2-fm))