diff --git a/autogl/datasets/utils.py b/autogl/datasets/utils.py
index 8b677ee..afd1128 100644
--- a/autogl/datasets/utils.py
+++ b/autogl/datasets/utils.py
@@ -1,3 +1,4 @@
+from pdb import set_trace
 import torch
 import numpy as np
 from torch_geometric.data import DataLoader
@@ -37,32 +38,33 @@ def random_splits_mask(dataset, train_ratio=0.2, val_ratio=0.4, seed=None):
     assert (
         train_ratio + val_ratio <= 1
     ), "the sum of train_ratio and val_ratio is larger than 1"
-    data = dataset[0]
-    r_s = torch.get_rng_state()
-    if torch.cuda.is_available():
-        r_s_cuda = torch.cuda.get_rng_state()
-    if seed is not None:
-        torch.manual_seed(seed)
+    _dataset=[d for d in dataset]
+    for data in _dataset:
+        r_s = torch.get_rng_state()
         if torch.cuda.is_available():
-            torch.cuda.manual_seed(seed)
-
-    perm = torch.randperm(data.num_nodes)
-    train_index = perm[: int(data.num_nodes * train_ratio)]
-    val_index = perm[
-        int(data.num_nodes * train_ratio) : int(
-            data.num_nodes * (train_ratio + val_ratio)
-        )
-    ]
-    test_index = perm[int(data.num_nodes * (train_ratio + val_ratio)) :]
-    data.train_mask = index_to_mask(train_index, size=data.num_nodes)
-    data.val_mask = index_to_mask(val_index, size=data.num_nodes)
-    data.test_mask = index_to_mask(test_index, size=data.num_nodes)
+            r_s_cuda = torch.cuda.get_rng_state()
+        if seed is not None:
+            torch.manual_seed(seed)
+            if torch.cuda.is_available():
+                torch.cuda.manual_seed(seed)
+
+        perm = torch.randperm(data.num_nodes)
+        train_index = perm[: int(data.num_nodes * train_ratio)]
+        val_index = perm[
+            int(data.num_nodes * train_ratio) : int(
+                data.num_nodes * (train_ratio + val_ratio)
+            )
+        ]
+        test_index = perm[int(data.num_nodes * (train_ratio + val_ratio)) :]
+        data.train_mask = index_to_mask(train_index, size=data.num_nodes)
+        data.val_mask = index_to_mask(val_index, size=data.num_nodes)
+        data.test_mask = index_to_mask(test_index, size=data.num_nodes)
 
-    torch.set_rng_state(r_s)
-    if torch.cuda.is_available():
-        torch.cuda.set_rng_state(r_s_cuda)
+        torch.set_rng_state(r_s)
+        if torch.cuda.is_available():
+            torch.cuda.set_rng_state(r_s_cuda)
 
-    dataset.data, dataset.slices = dataset.collate([d for d in dataset])
+    dataset.data, dataset.slices = dataset.collate(_dataset)
     # while type(dataset.data.num_nodes) == list:
     #    dataset.data.num_nodes = dataset.data.num_nodes[0]
     # dataset.data.num_nodes = dataset.data.num_nodes[0]