[PT FE] Fix segfault when prim::If can't be converted (#24914)

### Details:
- *When scripting upsample creates `prim::If` depending from input
shape, to determine which version of upsample to use: 1d, 2d or 3d. This
is not supported by openvino, but this PR fixes segfault that happen in
this case.*

### Tickets:
 - *#24270*

src/frontends/pytorch/src/pt_framework_node.hpp

@@ -20,8 +20,9 @@ public:
     PtFrameworkNode(const std::shared_ptr<TorchDecoder>& decoder,
                     const OutputVector& inputs,
                     size_t output_size,
-                    bool is_reverseprop = false)
-        : ov::op::util::FrameworkNode(inputs, output_size, decoder->get_subgraph_size()),
+                    bool is_reverseprop = false,
+                    bool skip_subgraphs = false)
+        : ov::op::util::FrameworkNode(inputs, output_size, skip_subgraphs ? 0 : decoder->get_subgraph_size()),
           m_decoder(decoder) {
         ov::op::util::FrameworkNodeAttrs attrs;
         attrs.set_type_name("PTFrameworkNode");

src/frontends/pytorch/src/utils.cpp

@@ -213,8 +213,9 @@ namespace {
 std::shared_ptr<PtFrameworkNode> create_fw_node_with_exception(const NodeContext& context,
                                                                const ov::OutputVector& inputs,
                                                                size_t num_outputs,
-                                                               const std::string& exception_message) {
-    auto fw_node = std::make_shared<PtFrameworkNode>(context.get_decoder(), inputs, num_outputs);
+                                                               const std::string& exception_message,
+                                                               bool skip_subgraphs = false) {
+    auto fw_node = std::make_shared<PtFrameworkNode>(context.get_decoder(), inputs, num_outputs, false, skip_subgraphs);
     context.mark_node(fw_node);
     auto attrs = fw_node->get_attrs();
     std::string message(exception_message);
@@ -229,7 +230,8 @@ std::shared_ptr<PtFrameworkNode> create_fw_node_with_exception(const NodeContext
 }  // namespace
 
 OutputVector make_framework_node_ignore_bodies(const NodeContext& context, const std::string& exception) {
-    auto fw_node = create_fw_node_with_exception(context, context.inputs(), context.get_output_size() + 1, exception);
+    auto fw_node =
+        create_fw_node_with_exception(context, context.inputs(), context.get_output_size() + 1, exception, true);
     return fw_node->outputs();
 }
 

tests/layer_tests/pytorch_tests/test_upsample.py

@@ -214,4 +214,39 @@ class TestUpsample2DListSizes(PytorchLayerTest):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_upsample2d_list_sizes(self, mode, ie_device, precision, ir_version):
-        self._test(*self.create_model(mode), ie_device, precision, ir_version, trace_model=True)
+        self._test(*self.create_model(mode), ie_device,
+                   precision, ir_version, trace_model=True)
+
+
+class TestUpsampleScripted(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 200, 200).astype(np.float32),)
+
+    def create_model(self):
+        import torch.nn as nn
+
+        class TestModel(nn.Module):
+            def __init__(self, n_channels, n_classes):
+                super().__init__()
+                self.n_channels = n_channels
+                self.n_classes = n_classes
+
+                self.cv1 = nn.Conv2d(n_channels, 16, kernel_size=3, padding=1)
+                self.mp1 = nn.MaxPool2d((2, 2), (2, 2))
+                self.up = nn.Upsample(scale_factor=2.)
+
+            def forward(self, x):
+                x1 = self.cv1(x)
+                x2 = self.mp1(x1)
+                x3 = self.up(x2)
+                return x3
+
+        return TestModel(1, 3), None, ["prim::If", "aten::upsample_nearest1d", "aten::upsample_nearest2d", "aten::upsample_nearest3d"]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.xfail(reason="Scripted upsample is not supported")
+    def test_upsample_scripted(self, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device,
+                   precision, ir_version, trace_model=False)