Thank you for your reply. I am still getting this error and not sure how to fix it, here is my code:
if name == “main”:
#process & create the dataset files
parser = argparse.ArgumentParser()
# system
parser.add_argument("--feature", type=str, default="glove", help="glove | all")
#no use of user_type for now
parser.add_argument("--user_type", type=str, default="hate", help="hate | suspend")
parser.add_argument("--model_type", type=str, default="sage", help="sage | gat")
parser.add_argument("--epoch", type=int, default=201)
args = parser.parse_args()
assert(args.feature in ['glove', 'all'])
assert(args.user_type in ['hate', 'suspend'])
assert(args.model_type in ['sage', 'gat'])
print("====information of experiment====")
print("FEATURE: ", args.feature, "classification_type:", args.user_type, "MODEL:", args.model_type)
print("====end information of experiment====")
dataset = construct_dataset(args.feature)
model_type = args.model_type
index0, index1, index2,index3,index4,index5,index6,index7,index8,index9,index10,index11,index12,index13,index14,index15,index16,index17,index18,index19,index20,index21,index22,index23,index24,index25,index26,index27,index28 = get_labeled_index(feature_type=args.feature)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=123)
#y_all = [2] * len(hate_index)
y_all = [0] * len(index0)

#y_normal = [0] * len(normal_index)
y_1 = [1] * len(index1)
y_2 = [2] * len(index2)
y_3 = [3] * len(index3)
y_4 = [4] * len(index4)
y_5 = [5] * len(index5)
y_6 = [6] * len(index6)
y_7 = [7] * len(index7)
y_8 = [8] * len(index8)
y_9 = [9] * len(index9)
y_10 = [10] * len(index10)
y_11 = [11] * len(index11)
y_12 = [12] * len(index12)
y_13 = [13] * len(index13)
y_14 = [14] * len(index14)
y_15 = [15] * len(index15)
y_16 = [16] * len(index16)
y_17 = [17] * len(index17)
y_18 = [18] * len(index18)
y_19 = [19] * len(index19)
y_20 = [20] * len(index20)
y_21 = [21] * len(index21)
y_22 = [22] * len(index22)
y_23 = [23] * len(index23)
y_24 = [24] * len(index24)
y_25 = [25] * len(index25)
y_26 = [26] * len(index26)
y_27 = [27] * len(index27)
y_28 = [28] * len(index28)


#y_all.extend(y_normal)
y_all.extend(y_1)
y_all.extend(y_2)
y_all.extend(y_3)
y_all.extend(y_4)
y_all.extend(y_5)
y_all.extend(y_6)
y_all.extend(y_7)
y_all.extend(y_8)
y_all.extend(y_9)
y_all.extend(y_10)
y_all.extend(y_11)
y_all.extend(y_12)
y_all.extend(y_13)
y_all.extend(y_14)
y_all.extend(y_15)
y_all.extend(y_16)
y_all.extend(y_17)
y_all.extend(y_18)
y_all.extend(y_19)
y_all.extend(y_20)
y_all.extend(y_21)
y_all.extend(y_22)
y_all.extend(y_23)
y_all.extend(y_24)
y_all.extend(y_25)
y_all.extend(y_26)
y_all.extend(y_27)
y_all.extend(y_28)


all_index = []

#all_index.extend(hate_index)
all_index.extend(index0)
all_index.extend(index1)
all_index.extend(index2)
all_index.extend(index3)
all_index.extend(index4)
all_index.extend(index5)
all_index.extend(index6)
all_index.extend(index7)
all_index.extend(index8)
all_index.extend(index9)
all_index.extend(index10)
all_index.extend(index11)
all_index.extend(index12)
all_index.extend(index13)
all_index.extend(index14)
all_index.extend(index15)
all_index.extend(index16)
all_index.extend(index17)
all_index.extend(index18)
all_index.extend(index19)
all_index.extend(index20)
all_index.extend(index21)
all_index.extend(index22)
all_index.extend(index23)
all_index.extend(index24)
all_index.extend(index25)
all_index.extend(index26)
all_index.extend(index27)
all_index.extend(index28)



recall_test = []
accuracy_test = []
fscore_test = []
precision_test = []
all_index = np.array(all_index)
trail = 0
for train_i, test_i in skf.split(all_index, y_all):
    print("========begin trail {:01d}===========".format(trail))
    all_train_index = all_index[train_i]
    test_index = all_index[test_i]
    data = dataset[0]
    data.train_mask = torch.zeros(data.num_nodes, dtype=torch.long)
    data.train_mask[all_train_index] = 1
    data.test_mask = torch.zeros(data.num_nodes, dtype=torch.long)
    data.test_mask[test_index] = 1
    loader = NeighborSampler(data, size=[25], num_hops=1, batch_size=128, shuffle=True, add_self_loops=True)

Here is what is in the data exactly:

Screen Shot 2021-05-29 at 12.23.49 AM1091×546 53.9 KB

I would appreciate your help. Thank you.