input: Write a function to load a dataset, split it into features and label, train a decision tree classifier, and then use the trained model to predict the class label of a given set of data. Assume the dataset has 4 features: Length, Width, Height, and Weight.
  def predict(data):
      predicted_label = model.predict([data])
      return predicted_label
  
  assert predict([1, 2, 3, 4])==1
  assert predict([5, 6, 7, 8])==2
  assert predict([1, 1, 1, 1])==3
  assert predict([9, 9, 9, 9])==4
output: 
  def predict(data):
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
  
      iris = load_iris()
      X = iris.data
      y = iris.target
      X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
  
      predicted_label = model.predict([data])
      return predicted_label[0]
  