test case of random forest

Question

why is there hidden test cases which are problematic with my random forest code:
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
randomState = int(input())
n = int(input())
rows = []
for i in range(n):
rows.append([float(a) for a in input().split()])
X = np.array(rows)
y = np.array([int(a) for a in input().split()])
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state=randomState)
sra=RandomForestClassifier(n_estimators=5,random_state=randomState)
sra.fit(X_train,y_train)
print(sra.predict(X_test))

Answer 1

I dont know why...my answer is very similar but this piece of code is some different X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)