diff --git a/ttnn/cpp/ttnn/operations/eltwise/ternary/ternary_pybind.hpp b/ttnn/cpp/ttnn/operations/eltwise/ternary/ternary_pybind.hpp
index 6b2215f17d3..f3436efabb4 100644
--- a/ttnn/cpp/ttnn/operations/eltwise/ternary/ternary_pybind.hpp
+++ b/ttnn/cpp/ttnn/operations/eltwise/ternary/ternary_pybind.hpp
@@ -51,10 +51,11 @@ void bind_ternary_composite_float(py::module& module, const ternary_operation_t&
             Note : bfloat8_b/bfloat4_b supports only on TILE_LAYOUT
 
         Example:
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
-            >>> output = {1}(tensor1, tensor2, tensor3)
+            >>> value = 1.0
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> output = {1}(tensor1, tensor2, tensor3, value)
         )doc",
         operation.base_name(),
         operation.python_fully_qualified_name(),
@@ -110,10 +111,9 @@ void bind_ternary_where(py::module& module, const ternary_operation_t& operation
         Note : bfloat8_b/bfloat4_b supports only on TILE_LAYOUT
 
         Example:
-
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 0], [1, 0]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
             >>> output = {1}(tensor1, tensor2, tensor3)
         )doc",
         operation.base_name(),
@@ -217,9 +217,9 @@ void bind_ternary_lerp(py::module& module, const ternary_operation_t& operation,
             Note : bfloat8_b/bfloat4_b supports only on TILE_LAYOUT
 
             Example:
-                >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device)
-                >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
-                >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
+                >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 0], [1, 0]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+                >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+                >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
                 >>> output = {1}(tensor1, tensor2, tensor3/scalar)
         )doc",
         operation.base_name(),
@@ -287,9 +287,9 @@ void bind_ternary_mac(py::module& module, const ternary_operation_t& operation,
             Note : bfloat8_b/bfloat4_b supports only on TILE_LAYOUT
 
         Example:
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
             >>> output = {1}(tensor1, tensor2/scalar, tensor3/scalar)
         )doc",
         operation.base_name(),
diff --git a/ttnn/cpp/ttnn/operations/eltwise/ternary_backward/ternary_backward_pybind.hpp b/ttnn/cpp/ttnn/operations/eltwise/ternary_backward/ternary_backward_pybind.hpp
index f17839c8461..6bc3113a216 100644
--- a/ttnn/cpp/ttnn/operations/eltwise/ternary_backward/ternary_backward_pybind.hpp
+++ b/ttnn/cpp/ttnn/operations/eltwise/ternary_backward/ternary_backward_pybind.hpp
@@ -43,12 +43,12 @@ void bind_ternary_backward(py::module& module, const ternary_backward_operation_
             {3}
 
         Example:
-
-            >>> grad_tensor = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> output = {1}(grad_tensor, tensor1, tensor2, tensor3, float)
+            >>> value = 1.0
+            >>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> output = {1}(grad_tensor, tensor1, tensor2, tensor3, value)
         )doc",
         operation.base_name(),
         operation.python_fully_qualified_name(),
@@ -110,10 +110,10 @@ void bind_ternary_backward_op(py::module& module, const ternary_backward_operati
 
 
         Example:
-            >>> grad_tensor = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
+            >>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
             >>> output = {1}(grad_tensor, tensor1, tensor2, tensor3/scalar)
 
         )doc",
@@ -188,11 +188,10 @@ void bind_ternary_backward_optional_output(py::module& module, const ternary_bac
             {3}
 
         Example:
-
-            >>> grad_tensor = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
-            >>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
-            >>> tensor3 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
+            >>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor1 = ttnn.from_torch(torch.tensor([[1, 0], [1, 0]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
+            >>> tensor3 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
             >>> output = {1}(grad_tensor, tensor1, tensor2, tensor3)
         )doc",
         operation.base_name(),