diff --git a/test/onnx/exporter/test_small_models_e2e.py b/test/onnx/exporter/test_small_models_e2e.py
index fc9f8e51970..0135e7c282a 100644
--- a/test/onnx/exporter/test_small_models_e2e.py
+++ b/test/onnx/exporter/test_small_models_e2e.py
@@ -125,6 +125,33 @@ class DynamoExporterTest(common_utils.TestCase):
         onnx_testing.assert_onnx_program(onnx_program, args=(torch.tensor([0, 0]),))
         onnx_testing.assert_onnx_program(onnx_program, args=(torch.tensor([43, 43]),))
 
+    def test_onnx_export_control_flow_multi_outputs(self):
+        class CondModel(torch.nn.Module):
+            def forward(self, x):
+                z = torch.ones_like(x)
+
+                def true_fn(x, z):
+                    x = x + 1.0
+                    z = z * 1.0
+                    return x, z
+
+                def false_fn(x, z):
+                    x = x - 1.0
+                    z = z * 0.0
+                    return x, z
+
+                x = torch.cond(x.sum() > 0, true_fn, false_fn, (x, z))
+                return x, z
+
+        onnx_program = torch.onnx.export(
+            CondModel(),
+            (torch.tensor([1, 2]),),
+            dynamo=True,
+            fallback=False,
+        )
+        onnx_testing.assert_onnx_program(onnx_program)
+        onnx_testing.assert_onnx_program(onnx_program, args=(torch.tensor([-1, -2]),))
+
     def test_onnx_export_torchvision_ops(self):
         class VisionModel(torch.nn.Module):
             def __init__(self):
@@ -142,8 +169,6 @@ class DynamoExporterTest(common_utils.TestCase):
         onnx_program = self.export(VisionModel(), args)
         onnx_testing.assert_onnx_program(onnx_program)
 
-    # TODO(justinchuby): Test multi-output HOPs
-
     def test_empty(self):
         def func(x):
             return torch.empty(x.size(), dtype=torch.int64)
diff --git a/torch/onnx/_internal/exporter/_core.py b/torch/onnx/_internal/exporter/_core.py
index d1173093b2e..1e1c13b8fd5 100644
--- a/torch/onnx/_internal/exporter/_core.py
+++ b/torch/onnx/_internal/exporter/_core.py
@@ -645,15 +645,18 @@ def _handle_output_node(
         node_name_to_values: A mapping of FX node names to their produced ONNX ``Value``.
         graph_like: The ONNX graph at construction.
     """
-    output_value_name = node.args[0][0].name  # type: ignore[index,union-attr]
-    assert isinstance(
-        output_value_name, str
-    ), f"Bug: Expected {output_value_name!r} to be a string"
-    values = node_name_to_values[output_value_name]
-    if isinstance(values, Sequence):
-        graph_like.outputs.extend(values)
-        return
-    graph_like.outputs.append(values)
+    # node.args[0] can be a tuple with more than one elements. This happens when,
+    # for example, a subgraph has multiple outputs. We flatten them all as ONNX graph outputs
+    for output in node.args[0]:  # type: ignore[index,union-attr]
+        output_value_name = output.name  # type: ignore[union-attr]
+        assert isinstance(
+            output_value_name, str
+        ), f"Bug: Expected {output_value_name!r} to be a string"
+        values = node_name_to_values[output_value_name]
+        if isinstance(values, Sequence):
+            graph_like.outputs.extend(values)
+            return
+        graph_like.outputs.append(values)
 
 
 def _translate_fx_graph(