diff --git a/extra/torch_backend/backend.py b/extra/torch_backend/backend.py
index 87fd9a1203..80fc038c76 100644
--- a/extra/torch_backend/backend.py
+++ b/extra/torch_backend/backend.py
@@ -479,6 +479,7 @@ tiny_backend_out = {**{f"aten.{x}.out":getattr(Tensor,x) for x in simple_tensor_
   "aten.fmax.out": lambda input,other: Tensor.where(input.isnan() & ~other.isnan(), other, Tensor.where(~input.isnan() & other.isnan(), input, Tensor.maximum(input, other))),
   "aten.fmin.out": lambda input,other: Tensor.where(input.isnan() & ~other.isnan(), other, Tensor.where(~input.isnan() & other.isnan(), input, Tensor.minimum(input, other))),
   "aten.amax.out": lambda self,dim=None: self.max(axis=dim),
+  "aten.amin.out": lambda self,dim=None: self.min(axis=dim),
   # TODO: this gets the shape wrong
   #"aten.arange.start_out": Tensor.arange,
   "aten.lerp.Scalar_out": Tensor.lerp,
diff --git a/extra/torch_backend/test.py b/extra/torch_backend/test.py
index 236da279b9..10c077001d 100644
--- a/extra/torch_backend/test.py
+++ b/extra/torch_backend/test.py
@@ -103,6 +103,27 @@ class TestTorchBackend(unittest.TestCase):
     expected = np.array([[4.7, 12.9, 12.3], [16.9, 24.9, 23.6]], dtype=np.float32)
     np.testing.assert_equal(y3.cpu().numpy(), expected)
 
+
+  def test_amin(self):
+    x = torch.tensor([[[ 1.5,  2.3,  3.1,  4.7],
+                       [ 5.2,  6.8,  7.4,  12.9],
+                       [ 9.0, 12.3, 11.6, 10.1]],
+                      [[13.2, 16.9, 15.5, 14.1],
+                       [17.1, 24.9, 19.8, 20.2],
+                       [21.0, 22.3, 23.6, 18.4]]], device=device)
+
+    y1 = torch.amin(x)
+    expected = np.array([1.5], dtype=np.float32)
+    np.testing.assert_equal(y1.cpu().numpy(), expected)
+
+    y2 = torch.amin(x, dim=(1,2))
+    expected = np.array([1.5, 13.2], dtype=np.float32)
+    np.testing.assert_equal(y2.cpu().numpy(), expected)
+
+    y3 = torch.amin(x, dim=2)
+    expected = np.array([[1.5, 5.2, 9.0], [13.2, 17.1, 18.4]], dtype=np.float32)
+    np.testing.assert_equal(y3.cpu().numpy(), expected)
+
   def test_isfinite(self):
     a = torch.ones(4, device=device)
     np.testing.assert_equal(torch.isfinite(a).cpu().numpy(), [True, True, True, True])