[PT FE]: fix unflatten for list construct sizes (#18039)

src/frontends/pytorch/src/op/unflatten.cpp

@@ -25,6 +25,9 @@ OutputVector translate_unflatten(const NodeContext& context) {
     auto input = context.get_input(0);
     auto dim = context.get_input(1);
     auto sizes = context.get_input(2);
+    if (context.get_input_type(2).is<type::List>()) {
+        sizes = concat_list_construct(sizes);
+    }
     auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
     auto zero_1d = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
     auto one_1d = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1}));

tests/layer_tests/pytorch_tests/test_unflatten.py

@@ -32,4 +32,38 @@ class TestUnflatten(PytorchLayerTest):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_unflatten(self, dim, shape, dtype, ie_device, precision, ir_version):
-        self._test(*self.create_model(dim, shape), ie_device, precision, ir_version, kwargs_to_prepare_input={"dtype": dtype})
+        self._test(*self.create_model(dim, shape), ie_device, precision, ir_version, kwargs_to_prepare_input={"dtype": dtype})
+
+
+class TestUnflattenListSizes(PytorchLayerTest):
+    def _prepare_input(self, dtype):
+        return (np.random.uniform(0, 50, (6, 2, 4)).astype(dtype),)
+
+    def create_model(self, dim):
+        import torch
+
+        class aten_unflatten(torch.nn.Module):
+            def __init__(self, dim):
+                super(aten_unflatten, self).__init__()
+                self.dim = dim
+
+            def forward(self, x):
+                dim1, dim2, dim3 = x.shape
+                if self.dim == 0:
+                    sizes = [dim1, -1]
+                elif self.dim == 1:
+                    sizes = [dim2 // 2, -1]
+                else:
+                    sizes = [2, dim3 // 2, -1]
+                return x.unflatten(self.dim, sizes)
+
+        ref_net = None
+
+        return aten_unflatten(dim), ref_net, "aten::unflatten"
+
+    @pytest.mark.parametrize("dim", [0, 1, 2])
+    @pytest.mark.parametrize("dtype", ["float32", "int32"])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_unflatten(self, dim, dtype, ie_device, precision, ir_version):
+        self._test(*self.create_model(dim), ie_device, precision, ir_version, kwargs_to_prepare_input={"dtype": dtype})