diff --git a/tests/TTIR/test_basic_ops.py b/tests/TTIR/test_basic_ops.py
index 413b5c0f..58dff4e0 100644
--- a/tests/TTIR/test_basic_ops.py
+++ b/tests/TTIR/test_basic_ops.py
@@ -224,6 +224,16 @@ def module_transpose(a):
     verify_module(module_transpose, [(3, 3)])
 
 
+@pytest.mark.skip(
+    "Scalars currently not working due to issue https://github.com/tenstorrent/tt-xla/issues/73"
+)
+def test_scalar_type():
+    def module_scalar_type(a):
+        return a.shape[0]
+
+    verify_module(module_scalar_type, [(3, 3)])
+
+
 # Transpose op failing for higher ranks/dimensions.
 @pytest.mark.skip("Transpose op failing for higher ranks/dimensions.")
 def test_transpose_op_3d():
diff --git a/tests/infrastructure.py b/tests/infrastructure.py
index dd0ce931..42c16c97 100644
--- a/tests/infrastructure.py
+++ b/tests/infrastructure.py
@@ -2,10 +2,22 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from contextlib import contextmanager
 import jax
 import jax.numpy as jnp
 
 
+@contextmanager
+def run_on_cpu():
+    devices = jax.local_devices(backend="cpu")
+    assert len(devices) > 0
+    cpu = devices[0]
+
+    with jax.default_device(cpu):
+        yield
+
+
+# TODO(issue #80): Make this creation more explicit regarding the originating devices.
 def random_input_tensor(shape, key=42, on_device=False, dtype=jnp.float32):
     device_cpu = jax.devices("cpu")[0]
     with jax.default_device(device_cpu):
@@ -27,6 +39,14 @@ def compare_tensor_to_golden(
     tensor, golden, required_pcc=0.99, required_atol=1e-2, assert_on_error=True
 ):
     ret = True
+
+    # TODO (issue #81): Remove these reshapes once the PJRT can handle scalars.
+    if tensor.ndim == 0:
+        tensor = tensor.reshape((1,))
+    if golden.ndim == 0:
+        with run_on_cpu():
+            golden = golden.reshape((1,))
+
     if tensor.device != golden.device:
         tensor = jax.device_put(tensor, golden.device)
 
@@ -66,6 +86,6 @@ def verify_module(
     tt_inputs = [jax.device_put(cpu_input, tt_device) for cpu_input in cpu_inputs]
     graph = jax.jit(module)
     res = graph(*tt_inputs)
-    res_cpu = graph(*cpu_inputs)
-
+    with run_on_cpu():
+        res_cpu = graph(*cpu_inputs)
     compare_tensor_to_golden(res, res_cpu, required_pcc, required_atol)