[PT FE]: support mixed precision in floor divide (#16362)

* [PT FE]: support mixed precision in floor divide

* Update floordiv.cpp

src/frontends/pytorch/src/op/floor_divide.cpp

@@ -18,6 +18,7 @@ OutputVector translate_floor_divide(NodeContext& context) {
     num_inputs_check(context, 2, 2);
     auto x = context.get_input(0);
     auto y = context.get_input(1);
+    align_eltwise_input_types(context, x, y, true);
     auto div = context.mark_node(std::make_shared<v1::Divide>(x, y, true));
     return {context.mark_node(std::make_shared<v0::Floor>(div))};
 };

src/frontends/pytorch/src/op/floordiv.cpp

@@ -21,4 +21,4 @@ OutputVector translate_floordiv(NodeContext& context) {
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov

tests/layer_tests/pytorch_tests/test_floor_divide.py

@@ -23,7 +23,20 @@ class TestFloorDivide(PytorchLayerTest):
 
         ref_net = None
 
-        # return aten_floor_divide(), ref_net, "aten::floor_divide"
+        return aten_floor_divide(), ref_net, "aten::floor_divide"
+
+    def create_model_int(self):
+        import torch
+
+        class aten_floor_divide(torch.nn.Module):
+            def __init__(self):
+                super(aten_floor_divide, self).__init__()
+
+            def forward(self, input_tensor, other_tensor):
+                return torch.floor_divide(input_tensor.to(torch.int32), other_tensor.to(torch.int64))
+
+        ref_net = None
+
         return aten_floor_divide(), ref_net, "aten::floor_divide"
 
     @pytest.mark.parametrize('input_tensor', ([
@@ -42,4 +55,23 @@ class TestFloorDivide(PytorchLayerTest):
     def test_floor_divide(self, input_tensor, other_tensor, ie_device, precision, ir_version):
         self.input_tensor = input_tensor
         self.other_tensor = other_tensor
+        self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True)
+
+    @pytest.mark.parametrize('input_tensor', ([
+        np.random.randint(low=0, high=10, size=5).astype(np.float32),
+        np.random.randint(low=1, high=10, size=(5, 5, 1)).astype(np.float32),
+        np.random.randint(low=1, high=10, size=(1, 1, 5, 5)).astype(np.float32),
+    ]))
+    @pytest.mark.parametrize('other_tensor', ([
+        np.array([[2]]).astype(np.float32),
+        np.random.randint(low=1, high=10, size=5).astype(np.float32),
+        np.random.randint(low=1, high=10, size=(5, 1)).astype(np.float32),
+        np.random.randint(low=1, high=10, size=(1, 5)).astype(np.float32),
+    ]))
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_floor_divide_int(self, input_tensor, other_tensor, ie_device, precision, ir_version):
+        self.input_tensor = input_tensor
+        self.other_tensor = other_tensor
+        self.create_model = self.create_model_int
         self._test(*self.create_model(), ie_device, precision, ir_version)