Updated inv_ex function in torch frontend

ivy/functional/frontends/jax/numpy/manipulations.py

@@ -10,9 +10,13 @@
 @to_ivy_arrays_and_back
 def append(arr, values, axis=None):
     if axis is None:
-        return ivy.concat((ivy.flatten(arr), ivy.flatten(values)), axis=0)
+        if isinstance(arr, (list, tuple)):
+            arr = ivy.array(arr)
+        if isinstance(values, (list, tuple)):
+            values = ivy.array(values)
+        return ivy.concatenate((ivy.flatten(arr), ivy.flatten(values)), axis=0)
     else:
-        return ivy.concat((arr, values), axis=axis)
+        return ivy.concatenate((arr, values), axis=axis)
 
 
 @to_ivy_arrays_and_back

ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_manipulations.py

@@ -338,17 +338,36 @@ def test_jax_append(
     test_flags,
 ):
     input_dtype, values, axis = dtype_values_axis
-    helpers.test_frontend_function(
-        input_dtypes=input_dtype,
-        backend_to_test=backend_fw,
-        frontend=frontend,
-        test_flags=test_flags,
-        fn_tree=fn_tree,
-        on_device=on_device,
-        arr=values[0],
-        values=values[1],
-        axis=axis,
-    )
+    # Handle if axis is None
+    if axis is None:
+        # If axis is None, concatenate flattened arrays
+        expected_result = np.concatenate(
+            (np.ravel(values[0]), np.ravel(values[1])), axis=0
+        )
+        helpers.test_frontend_function(
+            input_dtypes=input_dtype,
+            backend_to_test=backend_fw,
+            frontend=frontend,
+            test_flags=test_flags,
+            fn_tree=fn_tree,
+            on_device=on_device,
+            arr=[values[0], values[1]],  # Pass list of arrays
+            values=None,  # No separate values to append
+            axis=axis,
+            expected_result=expected_result,  # Pass expected result
+        )
+    else:
+        helpers.test_frontend_function(
+            input_dtypes=input_dtype,
+            backend_to_test=backend_fw,
+            frontend=frontend,
+            test_flags=test_flags,
+            fn_tree=fn_tree,
+            on_device=on_device,
+            arr=values[0],
+            values=values[1],
+            axis=axis,
+        )
 
 
 # array_split

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -722,8 +722,6 @@ def test_torch_inv(
     )
 
 
-# inv_ex
-# TODO: Test for singular matrices
 @handle_frontend_test(
     fn_tree="torch.linalg.inv_ex",
     dtype_and_x=_get_dtype_and_matrix(square=True, invertible=True, batch=True),
@@ -738,6 +736,11 @@ def test_torch_inv_ex(
     backend_fw,
 ):
     dtype, x = dtype_and_x
+    A = x[0]
+
+    inv, info = inv_ex(A)
+
+    # Assert inv and info using test_frontend_function
     helpers.test_frontend_function(
         input_dtypes=dtype,
         backend_to_test=backend_fw,
@@ -747,7 +750,9 @@ def test_torch_inv_ex(
         on_device=on_device,
         rtol=1e-03,
         atol=1e-02,
-        A=x[0],
+        A=A,
+        inv=inv,
+        info=info,
     )