Fix broken test

README.md

@@ -46,11 +46,7 @@ started is to
 As a contributor to GPJax, you are expected to abide by our [code of
 conduct](docs/CODE_OF_CONDUCT.md). If you feel that you have either experienced or
 witnessed behaviour that violates this standard, then we ask that you report any such
-<<<<<<< HEAD
-behaviours though [this form](https://jaxgaussianprocesses.com/contact/) or reach out to
-=======
 behaviours through [this form](https://jaxgaussianprocesses.com/contact/) or reach out to
->>>>>>> ac475762faa0cb9c64a773d5d9d3506d1a3ebdf2
 one of the project's [_gardeners_](https://docs.jaxgaussianprocesses.com/GOVERNANCE/#roles).
 
 Feel free to join our [Slack
@@ -139,17 +135,10 @@ D = gpx.Dataset(X=x, y=y)
 # Construct the prior
 meanf = gpx.mean_functions.Zero()
 kernel = gpx.kernels.RBF()
-<<<<<<< HEAD
 prior = gpx.gps.Prior(mean_function=meanf, kernel = kernel)
 
 # Define a likelihood
 likelihood = gpx.likelihoods.Gaussian(num_datapoints = n)
-=======
-prior = gpx.Prior(mean_function=meanf, kernel=kernel)
-
-# Define a likelihood
-likelihood = gpx.Gaussian(num_datapoints=n)
->>>>>>> ac475762faa0cb9c64a773d5d9d3506d1a3ebdf2
 
 # Construct the posterior
 posterior = prior * likelihood

tests/test_objectives.py

@@ -6,6 +6,8 @@
 
 import gpjax as gpx
 from gpjax.dataset import Dataset
+from gpjax.gps import Prior
+from gpjax.likelihoods import Gaussian
 from gpjax.objectives import (
     ELBO,
     AbstractObjective,
@@ -90,7 +92,8 @@ def test_conjugate_loocv(
 
     # Build model
     p = Prior(
-        kernel=gpx.RBF(active_dims=list(range(num_dims))), mean_function=gpx.Constant()
+        kernel=gpx.kernels.RBF(active_dims=list(range(num_dims))),
+        mean_function=gpx.mean_functions.Constant(),
     )
     likelihood = Gaussian(num_datapoints=num_datapoints)
     post = p * likelihood
@@ -176,7 +179,9 @@ def test_collapsed_elbo(
     assert evaluation.shape == ()
 
     # Data on the full dataset should be the same as the marginal likelihood
-    q = gpx.CollapsedVariationalGaussian(posterior=p * likelihood, inducing_inputs=D.X)
+    q = gpx.variational_families.CollapsedVariationalGaussian(
+        posterior=p * likelihood, inducing_inputs=D.X
+    )
     mll = ConjugateMLL(negative=negative)
     expected_value = mll(p * likelihood, D)
     actual_value = negative_elbo(q, D)