diff --git a/core/partitioning/partitioning.cpp b/core/partitioning/partitioning.cpp
index 935a9013cb..eaa8b1ad1f 100644
--- a/core/partitioning/partitioning.cpp
+++ b/core/partitioning/partitioning.cpp
@@ -70,7 +70,7 @@ void setExplicitFallbackNodes(PartitioningCtx* ctx, torch::jit::Block* block) {
   const auto to_compile_sym = c10::Symbol::attr("to_compile");
 
   for (const auto n : nodes) {
-    if (n->kind() == torch::jit::prim::Constant) {
+    if (isConstantOrUninitialized(n)) {
       continue;
     }
 
@@ -107,7 +107,7 @@ void setNonTensorConnectedNodes(PartitioningCtx* ctx, std::vector<torch::jit::No
     q.pop();
     // for every node that produces this fallback node's NonTensor input, they should fallback too
     for (auto input : cur_node->inputs()) {
-      if (!isTensor(input) && input->node()->kind() != torch::jit::prim::Constant &&
+      if (!isTensor(input) && !isConstantOrUninitialized(input->node()) &&
           ctx->shouldNodeRunInTensorRT(input->node())) {
         ctx->setNodeExecutorDecision(input->node(), NodeExecutorDecision::kNON_TENSOR);
         q.push(input->node());
@@ -118,7 +118,7 @@ void setNonTensorConnectedNodes(PartitioningCtx* ctx, std::vector<torch::jit::No
       if (!isTensor(output)) {
         for (auto use : output->uses()) {
           auto node = use.user;
-          if (node->kind() != torch::jit::prim::Constant && ctx->shouldNodeRunInTensorRT(node)) {
+          if (!isConstantOrUninitialized(node) && ctx->shouldNodeRunInTensorRT(node)) {
             ctx->setNodeExecutorDecision(node, NodeExecutorDecision::kNON_TENSOR);
             q.push(node);
           }
@@ -128,11 +128,13 @@ void setNonTensorConnectedNodes(PartitioningCtx* ctx, std::vector<torch::jit::No
   }
 }
 
-std::set<torch::jit::Node*> getDependentNodes(torch::jit::Node* n) {
-  std::set<torch::jit::Node*> dependent_nodes;
+std::set<torch::jit::Node*> getUserNodes(torch::jit::Node* n) {
+  std::set<torch::jit::Node*> user_nodes;
   for (auto val : n->outputs()) {
     for (auto use : val->uses()) {
-      dependent_nodes.insert(use.user);
+      if (use.user->owningBlock()->owningNode())
+        user_nodes.insert(use.user->owningBlock()->owningNode());
+      user_nodes.insert(use.user);
     }
   }
   if (const auto* schema = n->maybeSchema()) {
@@ -142,13 +144,13 @@ std::set<torch::jit::Node*> getDependentNodes(torch::jit::Node* n) {
         for (auto use : n->inputs()[i]->uses()) {
           torch::jit::Node* use_node = use.user;
           if (use_node->isAfter(n)) {
-            dependent_nodes.insert(use_node);
+            user_nodes.insert(use_node);
           }
         }
       }
     }
   }
-  return dependent_nodes;
+  return user_nodes;
 }
 
 // Sub-function that traverses the entire block and check if TensorRT node sequence satisfy min_block_size
@@ -158,14 +160,14 @@ std::vector<torch::jit::Node*> traverseNodesForMinBlockSize(PartitioningCtx* ctx
   std::unordered_set<torch::jit::Node*> cur_trt_nodes_uses;
   std::vector<torch::jit::Node*> min_block_fallback_nodes;
   for (const auto n : nodes) {
-    if (n->kind() == torch::jit::prim::Constant) {
+    if (isConstantOrUninitialized(n)) {
       continue;
     }
 
     // check if current node fallback or not
     if (!ctx->shouldNodeRunInTorch(n)) {
       cur_trt_nodes.push_back(n);
-      auto dependent_nodes = getDependentNodes(n);
+      auto dependent_nodes = getUserNodes(n);
       cur_trt_nodes_uses.insert(dependent_nodes.begin(), dependent_nodes.end());
     } else {
       if (cur_trt_nodes_uses.count(n)) {
@@ -250,7 +252,7 @@ std::vector<torch::jit::Node*> getDependencyNodes(
     auto cur_val = q.front();
     q.pop();
     auto node = cur_val->node();
-    if (node->kind() != torch::jit::prim::Constant && !visited.count(node)) {
+    if (!isConstantOrUninitialized(node) && !visited.count(node)) {
       visited.insert(node);
       auto modifying_nodes = findModifyingNodes(cur_val, seg_block_nodes);
       stk.insert(stk.end(), modifying_nodes.rbegin(), modifying_nodes.rend());
@@ -454,10 +456,10 @@ void segmentGraph(PartitioningCtx* ctx, torch::jit::Block* block) {
   std::unordered_set<torch::jit::Node*> cur_pyt_nodes_uses;
   for (const auto n : nodes) {
     // Skip constant nodes as they are resources for both kinds of modules
-    if (n->kind() == torch::jit::prim::Constant) {
+    if (isConstantOrUninitialized(n)) {
       continue;
     }
-    auto dependent_nodes = getDependentNodes(n);
+    auto dependent_nodes = getUserNodes(n);
     // the outputs of trt subgraph shouldn't be collections
     if (ctx->shouldNodeRunInTensorRT(n)) {
       in_prog_trt_blk_nodes.push_back(n);
diff --git a/core/partitioning/partitioningctx/PartitioningCtx.cpp b/core/partitioning/partitioningctx/PartitioningCtx.cpp
index 73fae28e91..6f10c5159f 100644
--- a/core/partitioning/partitioningctx/PartitioningCtx.cpp
+++ b/core/partitioning/partitioningctx/PartitioningCtx.cpp
@@ -15,13 +15,15 @@ PartitioningCtx::PartitioningCtx(torch::jit::Block* b, PartitioningInfo info)
 }
 
 void PartitioningCtx::_load_nodes_into_decision_map(torch::jit::Block* b) {
-  if (b->owningNode() && b->owningNode()->kind() == torch::jit::prim::Loop)
+  // won't load nodes if these nodes are in prim::loop or if these nodes are 2-level nested
+  if (b->owningNode() &&
+      (b->owningNode()->kind() == torch::jit::prim::Loop || b->owningNode()->owningBlock()->owningNode()))
     return;
 
   original_blocks.push_back(b);
 
   for (const auto n : b->nodes()) {
-    if (n->kind() == torch::jit::prim::Constant) {
+    if (isConstantOrUninitialized(n)) {
       continue;
     }
     node_executor_decision_map[n] = NodeExecutorDecision::kUNKNOWN;
diff --git a/core/partitioning/partitioningctx/PartitioningCtx.h b/core/partitioning/partitioningctx/PartitioningCtx.h
index 91e376eab3..f3290d2aa3 100644
--- a/core/partitioning/partitioningctx/PartitioningCtx.h
+++ b/core/partitioning/partitioningctx/PartitioningCtx.h
@@ -71,6 +71,10 @@ struct PartitioningCtx {
 
 std::ostream& operator<<(std::ostream& os, const PartitioningCtx& s);
 
+inline bool isConstantOrUninitialized(torch::jit::Node* n) {
+  return n->kind() == torch::jit::prim::Constant || n->kind() == torch::jit::prim::Uninitialized;
+}
+
 } // namespace partitioning
 } // namespace core
 } // namespace torch_tensorrt
diff --git a/core/partitioning/segmentedblock/SegmentedBlock.cpp b/core/partitioning/segmentedblock/SegmentedBlock.cpp
index 249a293bc3..c55127713b 100644
--- a/core/partitioning/segmentedblock/SegmentedBlock.cpp
+++ b/core/partitioning/segmentedblock/SegmentedBlock.cpp
@@ -46,6 +46,12 @@ torch::jit::Value* SegmentedBlock::getOrAddInputForValue(torch::jit::Value* old_
       old_to_new_[old_value] = new_const->output();
       return new_const->output();
     }
+    if (node->kind() == torch::jit::prim::Uninitialized) {
+      auto new_uninitialized = g_->createUninitialized(old_value->type());
+      g_->block()->prependNode(new_uninitialized);
+      old_to_new_[old_value] = new_uninitialized->output();
+      return new_uninitialized->output();
+    }
     auto new_value = g_->block()->addInput();
     // every time when we addInput, we push back the corresponding lowering graph torch::jit::Value to our raw_inputs
     inputs_.push_back(old_value);
diff --git a/tests/core/partitioning/test_segmentation.cpp b/tests/core/partitioning/test_segmentation.cpp
index b365a95ad9..95dfd772c3 100644
--- a/tests/core/partitioning/test_segmentation.cpp
+++ b/tests/core/partitioning/test_segmentation.cpp
@@ -331,6 +331,44 @@ TEST(Partitioning, SegmentModelWithDependencyAwareness) {
       checkSegmentedBlockNodesMapping(ctx.partitioned_blocks.begin()->second, g, {{0, 2, 4}, {1, 3, 5}, {6, 7}}));
 }
 
+TEST(Partitioning, ContainUninitializedValueCorrectly) {
+  auto g = std::make_shared<torch::jit::Graph>();
+  auto x = g->insertInput(0, "x");
+  auto none_const_val = g->insertConstant(torch::jit::IValue());
+  auto ivalue_1 = g->insertConstant(torch::jit::IValue(1));
+  auto ivalue_2 = g->insertConstant(torch::jit::IValue(2));
+
+  auto uninitialized_node = g->createUninitialized(torch::jit::BoolType::get());
+  g->appendNode(uninitialized_node);
+
+  auto x_dim = g->create(torch::jit::aten::dim, {x}, 1);
+  g->appendNode(x_dim);
+  x_dim->output()->setType(torch::jit::IntType::get());
+
+  auto eq1 = g->create(torch::jit::aten::eq, {ivalue_1, x_dim->output()}, 1);
+  g->appendNode(eq1);
+  eq1->output()->setType(torch::jit::BoolType::get());
+
+  torch::jit::IValue except("EXCEPTION");
+  auto exception_val = g->insertConstant(except);
+  auto if_node = g->create(torch::jit::prim::If, {eq1->output()}, 1);
+  auto if_block_0 = if_node->addBlock();
+  auto exception_node = g->create(torch::jit::prim::RaiseException, {exception_val, none_const_val}, 0);
+  if_block_0->appendNode(exception_node);
+  if_block_0->registerOutput(uninitialized_node->output());
+
+  auto if_block_1 = if_node->addBlock();
+  if_block_1->registerOutput(eq1->output());
+
+  g->insertNode(if_node);
+
+  PartitioningInfo partitioning_info;
+  partitioning_info.enabled = true;
+  PartitioningCtx ctx(g->block(), partitioning_info);
+  segmentGraph(&ctx, g->block());
+  ASSERT_TRUE(checkSegmentedBlockNumber(ctx.partitioned_blocks.begin()->second, SegmentedBlock::kTorch, 2));
+}
+
 } // namespace tests
 } // namespace partitioning
 } // namespace core