[PT FE] Fix issue with adding Result to mutated tensor (#20690)

* [PT FE] Fix issue with adding Result to mutated tensor

* Add test

src/frontends/pytorch/src/translate_session.cpp

@@ -271,8 +271,10 @@ std::shared_ptr<Model> TranslateSession::convert_pytorch_model(
                 auto mutated_tensor = tensor_map->at(tensor_id);
                 // empty external_tensor_map means this is main body of the model and we don't want to create
                 // additional outputs in that case.
-                if (mutated_tensor.get_target_inputs().empty() && !external_tensor_map.empty())
+                if (!external_tensor_map.empty()) {
+                    OPENVINO_DEBUG << "Creating Result for mutated tensor  " << tensor_id;
                     results.push_back(std::make_shared<v0::Result>(tensor_map->at(tensor_id)));
+                }
             } else {
                 OPENVINO_DEBUG << "Mutated tensor with id " << tensor_id << " doesn't exist in inputs, skipping.";
             }

tests/layer_tests/pytorch_tests/test_loop.py

@@ -0,0 +1,45 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+import os
+
+import pytest
+import numpy as np
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestLoopWithAlias(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.randn(*self.shape).astype(np.float32),)
+
+    def create_model(self, n):
+        import torch
+
+        class loop_alias_model(torch.nn.Module):
+            def __init__(self, n):
+                super(loop_alias_model, self).__init__()
+                self.n = n
+
+            def forward(self, x):
+                N = x.shape[1]
+                res = torch.zeros(1, self.n, dtype=torch.long)
+                d = torch.ones(1, N) * 1e10
+                f = torch.zeros(1, dtype=torch.long)
+
+                for i in range(self.n):
+                    res[:, i] = f
+                    _d = torch.sum((x - x[0, f, :]) ** 2, -1)
+                    m = _d < d
+                    d[m] = _d[m]
+                    f = torch.max(d, -1)[1]
+                return res
+
+        return loop_alias_model(n), None, ["prim::Loop", "aten::copy_"]
+
+    @pytest.mark.parametrize("s,n", [([1, 1024, 3], 512), ([1, 512, 3], 128)])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_loop_alias(self, s, n, ie_device, precision, ir_version):
+        self.shape = s
+        self._test(*self.create_model(n), ie_device, precision,
+                   ir_version, use_convert_model=True)