Implementation of inv function in Paddle.

ivy/functional/frontends/paddle/linalg.py

@@ -99,6 +99,11 @@ def eigvalsh(x, UPLO="L", name=None):
     return ivy.eigvalsh(x, UPLO=UPLO)
 
 
+@to_ivy_arrays_and_back
+def inv(x, name=None):
+    return ivy.inv(x)
+
+
 @to_ivy_arrays_and_back
 def lu_unpack(lu_data, lu_pivots, unpack_datas=True, unpack_pivots=True, *, out=None):
     A = lu_data

ivy_tests/test_ivy/test_frontends/test_paddle/test_linalg.py

@@ -688,6 +688,44 @@ def test_paddle_eigvalsh(
     )
 
 
+# inv
+@handle_frontend_test(
+    fn_tree="paddle.linalg.inv",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=-100.0,
+        max_value=100.0,
+        shape=helpers.ints(min_value=2, max_value=10).map(lambda x: tuple([x, x])),
+    ).filter(
+        lambda x: "float16" not in x[0]
+        and "bfloat16" not in x[0]
+        and np.linalg.det(np.asarray(x[1][0])) != 0
+    ),
+    test_with_out=st.just(False),
+)
+def test_paddle_inv(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        rtol=1e-01,
+        atol=1e-01,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+    )
+
+
 @handle_frontend_test(
     fn_tree="paddle.lu_unpack",
     dtype_x=_get_dtype_and_square_matrix(real_and_complex_only=True),