diff --git a/examples/test_graph_nas_space.py b/examples/test_graph_nas_space.py
new file mode 100644
index 0000000..0d60f3c
--- /dev/null
+++ b/examples/test_graph_nas_space.py
@@ -0,0 +1,73 @@
+import sys
+from nni.nas.pytorch.mutables import Mutable
+sys.path.append('../')
+from torch_geometric.nn import GCNConv
+import torch
+from autogl.datasets import build_dataset_from_name
+from autogl.solver import AutoNodeClassifier
+from autogl.module.train import NodeClassificationFullTrainer
+from autogl.module.nas import Darts, OneShotEstimator
+from autogl.module.nas.space.graph_nas import *
+from autogl.module.train import Acc
+from autogl.module.nas.algorithm.enas import Enas
+from autogl.module.nas.algorithm.rl import *
+from autogl.module.nas.estimator.one_shot import TrainEstimator
+import logging
+import numpy as np
+from tqdm import  tqdm
+if __name__ == '__main__':
+    logging.getLogger().setLevel(logging.WARNING)
+    dataset = build_dataset_from_name('cora')
+    space=GraphNasNodeClassificationSpace(hidden_dim=16,search_act_con=True,layer_number=2)
+    space.instantiate(input_dim=dataset[0].x.shape[1],
+                output_dim=dataset.num_classes,)
+    estim=TrainEstimator()
+    # solver.fit(dataset)
+    # solver.get_leaderboard().show()
+    # out = solver.predict_proba()
+    
+    # print('acc on cora', Acc.evaluate(out, dataset[0].y[dataset[0].test_mask].detach().numpy()))
+    class Tmp:
+        def __init__(self,space):
+            self.model = space
+            self.nas_modules = []
+            k2o = get_module_order(self.model)
+            replace_layer_choice(self.model, PathSamplingLayerChoice, self.nas_modules)
+            replace_input_choice(self.model, PathSamplingInputChoice, self.nas_modules)
+            self.nas_modules = sort_replaced_module(k2o, self.nas_modules)
+    
+    t=Tmp(space)
+    print(t.nas_modules)
+    nm=t.nas_modules
+    selection_range={}
+    for k,v in nm:
+        selection_range[k]=len(v)
+    ks=list(selection_range.keys())
+    selections=[]
+    def dfs(selection,d):
+        if d>=len(ks):
+            selections.append(selection.copy())
+            return 
+        k=ks[d]
+        r=selection_range[k]
+        for i in range(r):
+            selection[k]=i
+            dfs(selection,d+1)
+    dfs({},0)
+    print(f'#selections {len(selections)}')
+    device=torch.device('cuda:0')
+    accs=[]
+    from datetime import datetime
+    timestamp=datetime.now().strftime('%m%d-%H-%M-%S')
+    log=open(f'acclog{timestamp}.txt','w')
+    with tqdm(selections) as bar:
+        for selection in bar:
+            arch=space.export(selection,device)
+            m,l=estim.infer(arch,dataset,'test')
+            bar.set_postfix(m=m,l=l.item())
+            log.write(f'{arch}\n{selection}\n{m},{l}\n')
+            log.flush()
+            accs.append(m)
+
+    np.save(f'space_acc{timestamp}',np.array(accs))
+    print(f'max acc {np.max(accs)}')
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