diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 03fe680ffcd..5c67cfea404 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -62,7 +62,6 @@ jobs:
           - |
             pymc/tests/tuning/test_scaling.py
             pymc/tests/tuning/test_starting.py
-            pymc/tests/test_shared.py
             pymc/tests/test_sampling.py
             pymc/tests/distributions/test_dist_math.py
             pymc/tests/distributions/test_transform.py
diff --git a/pymc/tests/test_model.py b/pymc/tests/test_model.py
index 70f714aa9dc..fc03bf2182f 100644
--- a/pymc/tests/test_model.py
+++ b/pymc/tests/test_model.py
@@ -1373,3 +1373,14 @@ def test_missing_symmetric():
     logp_inputs = list(graph_inputs([logp]))
     assert x_obs_vv in logp_inputs
     assert x_unobs_vv in logp_inputs
+
+
+class TestShared(SeededTest):
+    def test_deterministic(self):
+        with pm.Model() as model:
+            data_values = np.array([0.5, 0.4, 5, 2])
+            X = aesara.shared(np.asarray(data_values, dtype=aesara.config.floatX), borrow=True)
+            pm.Normal("y", 0, 1, observed=X)
+            assert np.all(
+                np.isclose(model.compile_logp(sum=False)({}), st.norm().logpdf(data_values))
+            )
diff --git a/pymc/tests/test_sampling.py b/pymc/tests/test_sampling.py
index 204a35300b3..48239be3fc0 100644
--- a/pymc/tests/test_sampling.py
+++ b/pymc/tests/test_sampling.py
@@ -2302,3 +2302,37 @@ def test_float32(self):
                 with warnings.catch_warnings():
                     warnings.filterwarnings("ignore", ".*number of samples.*", UserWarning)
                     pm.sample(draws=10, tune=10, chains=1, step=sampler())
+
+
+class TestShared(SeededTest):
+    def test_sample(self):
+        x = np.random.normal(size=100)
+        y = x + np.random.normal(scale=1e-2, size=100)
+
+        x_pred = np.linspace(-3, 3, 200)
+
+        x_shared = aesara.shared(x)
+
+        with pm.Model() as model:
+            b = pm.Normal("b", 0.0, 10.0)
+            pm.Normal("obs", b * x_shared, np.sqrt(1e-2), observed=y, shape=x_shared.shape)
+            prior_trace0 = pm.sample_prior_predictive(1000)
+
+            idata = pm.sample(1000, tune=1000, chains=1)
+            pp_trace0 = pm.sample_posterior_predictive(idata)
+
+            x_shared.set_value(x_pred)
+            prior_trace1 = pm.sample_prior_predictive(1000)
+            pp_trace1 = pm.sample_posterior_predictive(idata)
+
+        assert prior_trace0.prior["b"].shape == (1, 1000)
+        assert prior_trace0.prior_predictive["obs"].shape == (1, 1000, 100)
+        np.testing.assert_allclose(
+            x, pp_trace0.posterior_predictive["obs"].mean(("chain", "draw")), atol=1e-1
+        )
+
+        assert prior_trace1.prior["b"].shape == (1, 1000)
+        assert prior_trace1.prior_predictive["obs"].shape == (1, 1000, 200)
+        np.testing.assert_allclose(
+            x_pred, pp_trace1.posterior_predictive["obs"].mean(("chain", "draw")), atol=1e-1
+        )
diff --git a/pymc/tests/test_shared.py b/pymc/tests/test_shared.py
deleted file mode 100644
index ce91c93c21f..00000000000
--- a/pymc/tests/test_shared.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#   Copyright 2020 The PyMC Developers
-#
-#   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 aesara
-import numpy as np
-import scipy.stats as st
-
-import pymc as pm
-
-from pymc.tests.helpers import SeededTest
-
-
-class TestShared(SeededTest):
-    def test_deterministic(self):
-        with pm.Model() as model:
-            data_values = np.array([0.5, 0.4, 5, 2])
-            X = aesara.shared(np.asarray(data_values, dtype=aesara.config.floatX), borrow=True)
-            pm.Normal("y", 0, 1, observed=X)
-            assert np.all(
-                np.isclose(model.compile_logp(sum=False)({}), st.norm().logpdf(data_values))
-            )
-
-    def test_sample(self):
-        x = np.random.normal(size=100)
-        y = x + np.random.normal(scale=1e-2, size=100)
-
-        x_pred = np.linspace(-3, 3, 200)
-
-        x_shared = aesara.shared(x)
-
-        with pm.Model() as model:
-            b = pm.Normal("b", 0.0, 10.0)
-            pm.Normal("obs", b * x_shared, np.sqrt(1e-2), observed=y, shape=x_shared.shape)
-            prior_trace0 = pm.sample_prior_predictive(1000)
-
-            idata = pm.sample(1000, tune=1000, chains=1)
-            pp_trace0 = pm.sample_posterior_predictive(idata)
-
-            x_shared.set_value(x_pred)
-            prior_trace1 = pm.sample_prior_predictive(1000)
-            pp_trace1 = pm.sample_posterior_predictive(idata)
-
-        assert prior_trace0.prior["b"].shape == (1, 1000)
-        assert prior_trace0.prior_predictive["obs"].shape == (1, 1000, 100)
-        np.testing.assert_allclose(
-            x, pp_trace0.posterior_predictive["obs"].mean(("chain", "draw")), atol=1e-1
-        )
-
-        assert prior_trace1.prior["b"].shape == (1, 1000)
-        assert prior_trace1.prior_predictive["obs"].shape == (1, 1000, 200)
-        np.testing.assert_allclose(
-            x_pred, pp_trace1.posterior_predictive["obs"].mean(("chain", "draw")), atol=1e-1
-        )