remove trailing _tensor from image kernel checks (#7908)

test/test_transforms_v2_refactored.py

@@ -459,7 +459,7 @@ class TestResize:
     @pytest.mark.parametrize("antialias", [True, False])
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, size, interpolation, use_max_size, antialias, dtype, device):
+    def test_kernel_image(self, size, interpolation, use_max_size, antialias, dtype, device):
         if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
             return
 
@@ -830,7 +830,7 @@ class TestResize:
 class TestHorizontalFlip:
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, dtype, device):
+    def test_kernel_image(self, dtype, device):
         check_kernel(F.horizontal_flip_image, make_image(dtype=dtype, device=device))
 
     @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
@@ -980,7 +980,7 @@ class TestAffine:
     )
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, param, value, dtype, device):
+    def test_kernel_image(self, param, value, dtype, device):
         if param == "fill":
             value = adapt_fill(value, dtype=dtype)
         self._check_kernel(
@@ -1280,7 +1280,7 @@ class TestAffine:
 class TestVerticalFlip:
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, dtype, device):
+    def test_kernel_image(self, dtype, device):
         check_kernel(F.vertical_flip_image, make_image(dtype=dtype, device=device))
 
     @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
@@ -1404,7 +1404,7 @@ class TestRotate:
     )
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, param, value, dtype, device):
+    def test_kernel_image(self, param, value, dtype, device):
         kwargs = {param: value}
         if param != "angle":
             kwargs["angle"] = self._MINIMAL_AFFINE_KWARGS["angle"]
@@ -2382,7 +2382,7 @@ class TestElastic:
     )
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_kernel_image_tensor(self, param, value, dtype, device):
+    def test_kernel_image(self, param, value, dtype, device):
         image = make_image_tensor(dtype=dtype, device=device)
 
         check_kernel(