[PT FE]: fix constant folding for dequantization (#18190)

* [PT FE]: fix constant folding for dequantization

* add test

tests/layer_tests/pytorch_tests/test_tuple_construct.py

@@ -54,4 +54,30 @@ class TestTupleConstruct(PytorchLayerTest):
     @pytest.mark.parametrize("case", ["single", "multiple", "none", "list", "list_and_tuple"])
     @pytest.mark.nightly
     def test_tuple_construct(self, case, ie_device, precision, ir_version):
-        self._test(*self.create_model(case), ie_device, precision, ir_version)
+        self._test(*self.create_model(case), ie_device, precision, ir_version)
+    
+
+class TestTupleConstructTupleUnpack(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32),)
+
+    def create_model(self):
+        import torch
+
+        class prim_tuple_construct_tuple_unpack(torch.nn.Module):
+
+            def forward(self, x):
+                x1, x2, x3, x4, x5 = self.prepare_input(x)
+                return x1, x2, x3, x4, x5
+            
+            def prepare_input(self, x):
+                return x, x + 2, None, x.reshape(-1), (x * 10).to(torch.int32)
+
+
+        ref_net = None
+
+        return prim_tuple_construct_tuple_unpack(), ref_net, ["prim::TupleConstruct", "prim::TupleUnpack"]
+
+    @pytest.mark.nightly
+    def test_tuple_construct_unpack(self, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device, precision, ir_version, freeze_model=False)