input: Write a function to predict the class labels of a given set of data. The dataset contains 4 features: Length, Width, Height, and Weight. If the dataset is loaded as a pandas DataFrame and split into features and labels, use a DecisionTreeClassifier to fit the model and predict the class labels for any new data entry. The function should return the predicted label.
  assert predict([10, 5, 5, 100]) == [0]
  assert predict([20, 10, 10, 200]) == [1]
  assert predict([15, 7, 7, 150]) == [0]
  assert predict([30, 15, 15, 300]) == [2]
  assert predict([25, 12, 12, 250]) == [1]
output: def predict(data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      dataset = pd.DataFrame({
          'Length': [10, 20, 15, 30, 25],
          'Width': [5, 10, 7, 15, 12],
          'Height': [5, 10, 7, 15, 12],
          'Weight': [100, 200, 150, 300, 250],
          'Label': [0, 1, 0, 2, 1]
      })
      features = dataset[['Length', 'Width', 'Height', 'Weight']]
      labels = dataset['Label']
      model = DecisionTreeClassifier()
      model.fit(features, labels)
      return model.predict([data])