diff --git a/e2eshark/pytorch/operators/gridsampler/model.py b/e2eshark/pytorch/operators/gridsampler/model.py
old mode 100755
new mode 100644
index 6c24280c0..3674195a3
--- a/e2eshark/pytorch/operators/gridsampler/model.py
+++ b/e2eshark/pytorch/operators/gridsampler/model.py
@@ -22,13 +22,16 @@ def __init__(self):
         super().__init__()
 
     def forward(self, x, g):
-        z = nn.functional.grid_sample(x, g, mode="bilinear", padding_mode="zeros", align_corners=True)
+        interpolation_mode=0,
+        padding_mode=0,
+        align_corners=False,
+        z = nn.functional.grid_sample(x, g, mode="bilinear", padding_mode="zeros", align_corners=False)
         return z
 
 model = op_gridsampler()
-
-X = torch.rand(4, 7, 8, 11)
-Y = torch.rand(4, 9, 13, 2)*2-1
+# torch.manual_seed(42)
+X = torch.rand(7, 8, 12, 4)
+Y = torch.rand(7, 11, 13, 2)*2.0-1.0
 Z = model(X, Y)
 
 E2ESHARK_CHECK["input"] = [X, Y]
diff --git a/e2eshark/pytorch/operators/gridsampler2/model.py b/e2eshark/pytorch/operators/gridsampler2/model.py
new file mode 100755
index 000000000..d6076d7d5
--- /dev/null
+++ b/e2eshark/pytorch/operators/gridsampler2/model.py
@@ -0,0 +1,38 @@
+# Copyright 2024 Advanced Micro Devices, Inc.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import sys, argparse
+import torch
+import torch.nn as nn
+import torch_mlir
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+class op_gridsampler(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, g):
+        z = nn.functional.grid_sample(x, g, mode="bilinear", padding_mode="zeros", align_corners=True)
+        return z
+
+model = op_gridsampler()
+torch.manual_seed(42)
+X = torch.rand(4, 7, 8, 11)
+Y = torch.rand(4, 9, 13, 2)*2-1
+Z = model(X, Y)
+
+E2ESHARK_CHECK["input"] = [X, Y]
+E2ESHARK_CHECK["output"] =  Z
+print("Input:", X)
+print("Grid:", Y)
+print("Output:", Z)