feat: add mod to paddle tensor (ivy-llc#23683)

lgtm!

ivy/functional/frontends/paddle/tensor/tensor.py

@@ -611,6 +611,10 @@ def isclose(self, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
     def floor_divide(self, y, name=None):
         return paddle_frontend.floor_divide(self, y)
 
+    @with_supported_dtypes({"2.5.1 and below": ("int32", "int64")}, "paddle")
+    def mod(self, y, name=None):
+        return paddle_frontend.Tensor(ivy.fmod(self._ivy_array, _to_ivy_array(y)))
+
     # cond
     @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
     def cond(self, p=None, name=None):

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py

@@ -2966,6 +2966,42 @@ def test_paddle_tensor_minimum(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="mod",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        shared_dtype=True,
+        min_value=0,
+        exclude_min=True,
+    ),
+)
+def test_paddle_tensor_mod(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={"data": x[0]},
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={"y": x[1]},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="paddle.to_tensor",