diff --git a/test/tree/integration/test_torch.py b/test/tree/integration/test_torch.py
index 19871568657cd37625d9c83f2ea1cabfea0e6790..a9a0a8e1cb21ec47f0726fa99cd3c73becb6b39b 100644
--- a/test/tree/integration/test_torch.py
+++ b/test/tree/integration/test_torch.py
@@ -1,3 +1,4 @@
+from typing import Tuple, Mapping
 from unittest import skipUnless
 
 import pytest
@@ -94,3 +95,69 @@ class TestTreeIntegrationTorch:
 
         assert _t_isclose(foox(x, y), x + y).all() == \
                FastTreeValue({'a': torch.tensor(True), 'b': torch.tensor(True)})
+
+    @skipUnless(vpip('torch') >= '2.0.0' and OS.linux and vpython < '3.11', 'Torch 2 on linux platform required')
+    def test_with_module(self):
+        from torch import nn
+
+        class MLP(nn.Module):
+            def __init__(self, in_features: int, out_features: int, layers: Tuple[int, ...] = (1024,)):
+                nn.Module.__init__(self)
+                self.in_features = in_features
+                self.out_features = out_features
+                self.layers = layers
+                ios = [self.in_features, *self.layers, self.out_features]
+                self.mlp = nn.Sequential(
+                    *(
+                        nn.Linear(in_, out_, bias=True)
+                        for in_, out_ in zip(ios[:-1], ios[1:])
+                    )
+                )
+
+            def forward(self, x):
+                return self.mlp(x)
+
+        class MultiHeadMLP(nn.Module):
+            def __init__(self, in_features: int, out_features: Mapping[str, int], layers: Tuple[int, ...] = (1024,)):
+                nn.Module.__init__(self)
+                self.in_features = in_features
+                self.out_features = out_features
+                self.layers = layers
+                _networks = {
+                    o_name: MLP(in_features, o_feat, layers)
+                    for o_name, o_feat in self.out_features.items()
+                }
+                self.mlps = nn.ModuleDict(_networks)
+                self._t_mlps = FastTreeValue(_networks)
+
+            def forward(self, x):
+                return self._t_mlps(x)
+
+        net = MultiHeadMLP(
+            20,
+            {'a': 10, 'b': 20, 'c': 14, 'd': 3},
+        )
+        net = torch.compile(net)
+
+        input1 = torch.randn(3, 20)
+        output1 = net(input1)
+        assert output1.shape == FastTreeValue({
+            'a': torch.Size([3, 10]),
+            'b': torch.Size([3, 20]),
+            'c': torch.Size([3, 14]),
+            'd': torch.Size([3, 3]),
+        })
+
+        input2 = FastTreeValue.func()(torch.randn)(FastTreeValue({
+            'a': (3, 20),
+            'b': (4, 20),
+            'c': (20,),
+            'd': (2, 5, 20),
+        }))
+        output2 = net(input2)
+        assert output2.shape == FastTreeValue({
+            'a': torch.Size([3, 10]),
+            'b': torch.Size([4, 20]),
+            'c': torch.Size([14]),
+            'd': torch.Size([2, 5, 3]),
+        })