diff --git a/src/beignet/polynomial/__get_domain.py b/src/beignet/polynomial/__get_domain.py
deleted file mode 100644
index 53e263e65f..0000000000
--- a/src/beignet/polynomial/__get_domain.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-from torch import Tensor
-
-
-def _get_domain(input: Tensor) -> Tensor:
-    if torch.is_complex(input):
-        output = torch.tensor(
-            [
-                torch.min(input.real) + 1j * torch.min(input.imag),
-                torch.max(input.real) + 1j * torch.max(input.imag),
-            ],
-        )
-        return output
-    else:
-        output = torch.tensor(
-            [
-                torch.min(input),
-                torch.max(input),
-            ],
-        )
-
-    return output
diff --git a/src/beignet/polynomial/__init__.py b/src/beignet/polynomial/__init__.py
index 445a3ea2c2..3020f7bafd 100644
--- a/src/beignet/polynomial/__init__.py
+++ b/src/beignet/polynomial/__init__.py
@@ -10,7 +10,6 @@
 from .__div import _div
 from .__fit import _fit
 from .__from_roots import _from_roots
-from .__get_domain import _get_domain
 from .__map_domain import _map_domain
 from .__map_parameters import _map_parameters
 from .__normed_hermite_e_n import _normed_hermite_e_n
@@ -284,7 +283,6 @@
     "_div",
     "_fit",
     "_from_roots",
-    "_get_domain",
     "_map_domain",
     "_map_parameters",
     "_normed_hermite_e_n",
diff --git a/tests/beignet/polynomial/test___get_domain.py b/tests/beignet/polynomial/test___get_domain.py
deleted file mode 100644
index 1e6d9b6a68..0000000000
--- a/tests/beignet/polynomial/test___get_domain.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import beignet.polynomial
-import torch
-
-
-def test__get_domain():
-    torch.testing.assert_close(
-        beignet.polynomial._get_domain(
-            torch.tensor([1.0, 10.0, 3.0, -1.0]),
-        ),
-        torch.tensor([-1.0, 10.0]),
-    )
-
-    torch.testing.assert_close(
-        beignet.polynomial._get_domain(
-            torch.tensor([1 + 1j, 1 - 1j, 0.0, 2.0]),
-        ),
-        torch.tensor([-0 - 1j, 2 + 1j]),
-    )