diff --git a/ivy/functional/backends/jax/gradients.py b/ivy/functional/backends/jax/gradients.py
index c75a8b751d4e7..5e96bdd28c101 100644
--- a/ivy/functional/backends/jax/gradients.py
+++ b/ivy/functional/backends/jax/gradients.py
@@ -49,7 +49,7 @@ def _forward_fn(
                 ivy.index_nest(xs, grad_idx), ivy.is_array
             )
             for idx in xs_grad_arr_idx:
-                xs_grad_arr_idxs.append(grad_idx + idx)
+                xs_grad_arr_idxs.append(list(grad_idx) + idx)
         ivy.set_nest_at_indices(xs, xs_grad_arr_idxs, x_arr_values)
     elif ivy.is_array(xs):
         xs = x
@@ -75,8 +75,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ):
     # Conversion of required arrays to float variables and duplicate index chains
     (
diff --git a/ivy/functional/backends/mxnet/gradients.py b/ivy/functional/backends/mxnet/gradients.py
index a832aa2d0bab8..203640b7df2ef 100644
--- a/ivy/functional/backends/mxnet/gradients.py
+++ b/ivy/functional/backends/mxnet/gradients.py
@@ -2,7 +2,7 @@
 signature."""
 
 # global
-from typing import Optional, Sequence, Union
+from typing import Sequence, Union
 import mxnet as mx
 
 # local
@@ -27,8 +27,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ):
     raise IvyNotImplementedException()
 
diff --git a/ivy/functional/backends/numpy/gradients.py b/ivy/functional/backends/numpy/gradients.py
index 9b1cb295b6d6e..d6ba1e9b55bd7 100644
--- a/ivy/functional/backends/numpy/gradients.py
+++ b/ivy/functional/backends/numpy/gradients.py
@@ -3,7 +3,7 @@
 
 # global
 import logging
-from typing import Optional, Sequence, Union
+from typing import Sequence, Union
 import ivy
 
 
@@ -31,8 +31,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ):
     logging.warning(
         "NumPy does not support autograd, "
diff --git a/ivy/functional/backends/paddle/gradients.py b/ivy/functional/backends/paddle/gradients.py
index 3f148343b03f1..ff3801646d31d 100644
--- a/ivy/functional/backends/paddle/gradients.py
+++ b/ivy/functional/backends/paddle/gradients.py
@@ -108,7 +108,7 @@ def grad_(x):
     {"2.5.1 and below": {"cpu": ("float16",)}}, backend_version
 )
 def execute_with_gradients(
-    func, xs, /, *, retain_grads=False, xs_grad_idxs=[[0]], ret_grad_idxs=[[0]]
+    func, xs, /, *, retain_grads=False, xs_grad_idxs=((0,),), ret_grad_idxs=((0,),)
 ):
     # Conversion of required arrays to float variables and duplicate index chains
     xs, xs_grad_idxs, xs1, required_duplicate_index_chains, _ = (
diff --git a/ivy/functional/backends/tensorflow/gradients.py b/ivy/functional/backends/tensorflow/gradients.py
index a006020b08d1c..da688a2dd71f5 100644
--- a/ivy/functional/backends/tensorflow/gradients.py
+++ b/ivy/functional/backends/tensorflow/gradients.py
@@ -68,8 +68,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ):
     # Conversion of required arrays to float variables and duplicate index chains
     xs, xs_grad_idxs, xs_required, required_duplicate_index_chains, _ = (
diff --git a/ivy/functional/backends/torch/gradients.py b/ivy/functional/backends/torch/gradients.py
index 18b1c139630a6..a1f84509bb8da 100644
--- a/ivy/functional/backends/torch/gradients.py
+++ b/ivy/functional/backends/torch/gradients.py
@@ -99,8 +99,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ):
     # Conversion of required arrays to float variables and duplicate index chains
     xs, xs_grad_idxs, xs1, required_duplicate_index_chains, _ = (
diff --git a/ivy/functional/ivy/gradients.py b/ivy/functional/ivy/gradients.py
index 2d2c735ae9afd..8b9d72f383c1c 100644
--- a/ivy/functional/ivy/gradients.py
+++ b/ivy/functional/ivy/gradients.py
@@ -406,8 +406,8 @@ def execute_with_gradients(
     /,
     *,
     retain_grads: bool = False,
-    xs_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
-    ret_grad_idxs: Optional[Sequence[Sequence[Union[str, int]]]] = [[0]],
+    xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
+    ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
 ) -> Tuple[ivy.Array, ivy.Array]:
     """Call function func with input of xs variables, and return the function
     result func_ret and the gradients of each output variable w.r.t each input
diff --git a/ivy/utils/assertions.py b/ivy/utils/assertions.py
index a90f2df63f698..0a5f653f7fd8f 100644
--- a/ivy/utils/assertions.py
+++ b/ivy/utils/assertions.py
@@ -136,7 +136,7 @@ def check_all_or_any_fn(
     *args,
     fn,
     type="all",
-    limit=[0],
+    limit=(0,),
     message="args must exist according to type and limit given",
     as_array=True,
 ):