#
# Copyright (c) 2021 The GPflux Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import abc

import numpy as np
import pytest
import tensorflow as tf
import tensorflow_probability as tfp

from gpflow.kullback_leiblers import gauss_kl

from gpflux.encoders import DirectlyParameterizedNormalDiag
from gpflux.layers import LatentVariableLayer, LayerWithObservations, TrackableLayer

tf.keras.backend.set_floatx("float64")

############
# Utilities
############


def _zero_one_normal_prior(w_dim):
    """ N(0, I) prior """
    return tfp.distributions.MultivariateNormalDiag(loc=np.zeros(w_dim), scale_diag=np.ones(w_dim))


def get_distributions_with_w_dim():
    distributions = []
    for d in [1, 5]:

        mean = 
np.zeros(d)