diff --git a/tests/ignite/__init__.py b/tests/ignite/__init__.py
index d553c222e58b..8f84e2e74b99 100644
--- a/tests/ignite/__init__.py
+++ b/tests/ignite/__init__.py
@@ -3,3 +3,14 @@
 
 def cpu_and_maybe_cuda():
     return ("cpu",) + (("cuda",) if torch.cuda.is_available() else ())
+
+
+def is_mps_available_and_functional():
+    if not torch.backends.mps.is_available():
+        return False
+    try:
+        # Try to allocate a small tensor on the MPS device
+        torch.tensor([1.0], device="mps")
+        return True
+    except RuntimeError:
+        return False
diff --git a/tests/ignite/distributed/utils/test_serial.py b/tests/ignite/distributed/utils/test_serial.py
index d61fa8b75695..6aef0de221e4 100644
--- a/tests/ignite/distributed/utils/test_serial.py
+++ b/tests/ignite/distributed/utils/test_serial.py
@@ -2,6 +2,7 @@
 
 import ignite.distributed as idist
 from ignite.distributed.comp_models.base import _torch_version_le_112
+from tests.ignite import is_mps_available_and_functional
 from tests.ignite.distributed.utils import (
     _sanity_check,
     _test_distrib__get_max_length,
@@ -12,7 +13,6 @@
     _test_distrib_new_group,
     _test_sync,
 )
-from ....utils_for_tests import is_mps_available_and_functional
 
 
 def test_no_distrib(capsys):
diff --git a/tests/ignite/engine/test_create_supervised.py b/tests/ignite/engine/test_create_supervised.py
index 2ecf7438b458..d9b0c161f75d 100644
--- a/tests/ignite/engine/test_create_supervised.py
+++ b/tests/ignite/engine/test_create_supervised.py
@@ -25,7 +25,7 @@
 )
 from ignite.metrics import MeanSquaredError
 
-from ...utils_for_tests import is_mps_available_and_functional  # type: ignore
+from tests.ignite import is_mps_available_and_functional
 
 
 class DummyModel(torch.nn.Module):
diff --git a/tests/utils_for_tests.py b/tests/utils_for_tests.py
deleted file mode 100644
index 3076d080bfb9..000000000000
--- a/tests/utils_for_tests.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import torch
-
-
-def is_mps_available_and_functional():
-    if not torch.backends.mps.is_available():
-        return False
-    try:
-        # Try to allocate a small tensor on the MPS device
-        torch.tensor([1.0], device="mps")
-        return True
-    except RuntimeError:
-        return False