diff --git a/core/conversion/conversion.cpp b/core/conversion/conversion.cpp
index bebcf794c8..ec7fdc79fb 100644
--- a/core/conversion/conversion.cpp
+++ b/core/conversion/conversion.cpp
@@ -398,7 +398,7 @@ void ConvertBlockToNetDef(
       EvaluateConditionalBlock(ctx, n);
     } else if (to_eval) {
       auto eval = EvaluateNode(ctx, n);
-      if (eval) {
+      if (eval && n->outputs().size() > 0) {
         if (n->outputs().size() > 1) { // For ListUnpack scenario
           if (eval.value().isTuple()) {
             auto eval_list = eval.value().toTuple();
diff --git a/core/conversion/evaluators/aten.cpp b/core/conversion/evaluators/aten.cpp
index 8adac76c38..e356dbf6be 100644
--- a/core/conversion/evaluators/aten.cpp
+++ b/core/conversion/evaluators/aten.cpp
@@ -285,6 +285,27 @@ auto aten_registrations TORCHTRT_UNUSED =
                     EvalOptions().validSchemas({
                         "aten::append.t(t[](a!) self, t(c -> *) el) -> (t[](a!))",
                     })})
+        .evaluator({c10::Symbol::fromQualString("aten::extend"),
+                    [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
+                      if (args.at(n->input(0)).IValue()->isList() && args.at(n->input(1)).IValue()->isList()) {
+                        auto self = args.at(n->input(0)).IValue()->to<c10::List<c10::IValue>>();
+                        auto other = args.at(n->input(1)).IValue()->to<c10::List<c10::IValue>>();
+                        const int64_t other_size = other.size();
+
+                        for (int64_t i = 0; i < other_size; i++) {
+                          self.push_back(other.get(i));
+                        }
+                        return self;
+                      } else {
+                        TORCHTRT_THROW_ERROR(
+                            "Unimplemented data type for aten::extend.t evaluator: "
+                            << args.at(n->input(0)).IValue()->type()->str() << ", "
+                            << args.at(n->input(1)).IValue()->type()->str());
+                      }
+                    },
+                    EvalOptions().validSchemas({
+                        "aten::extend.t(t[](a!) self, t[] other) -> ()",
+                    })})
         .evaluator({c10::Symbol::fromQualString("aten::neg"),
                     [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {
                       auto el = args.at(n->input(0)).unwrapToInt();
diff --git a/tests/core/conversion/evaluators/test_aten_evaluators.cpp b/tests/core/conversion/evaluators/test_aten_evaluators.cpp
index 41eb1e06be..c119c221f5 100644
--- a/tests/core/conversion/evaluators/test_aten_evaluators.cpp
+++ b/tests/core/conversion/evaluators/test_aten_evaluators.cpp
@@ -302,6 +302,32 @@ TEST(Evaluators, FloorFloatIntEvaluatesCorrectly) {
   ASSERT_TRUE(jit_results[0] == trt_results[0]);
 }
 
+TEST(Evaluators, ATenExtendEvaluatesCorrectly) {
+  const auto graph = R"IR(
+      graph(%0 : Tensor, %1 : Tensor):
+        %2 : int = prim::Constant[value=0]()
+        %3 : Tensor[] = prim::ListConstruct(%0)
+        %4 : Tensor[] = prim::ListConstruct(%1)
+        aten::extend(%3, %4)
+        %5 : Tensor = aten::cat(%3, %2)
+        return (%5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, &*g);
+
+  auto in0 = at::randint(1, 10, {3, 4}, {at::kCUDA});
+  auto in1 = at::randint(1, 10, {5, 4}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in0, in1});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in0, in1});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
 TEST(Evaluators, ATenAppendWithITensorEvaluatesCorrectly) {
   const auto graph = R"IR(
       graph(%0 : Tensor, %1 : Tensor):