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. Load the dataset from a CSV file, split it into features and label, train a DecisionTreeClassifier model on the data, and then use the trained model to predict the label for a new set of features.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([5, 6, 7, 8])==[1]
  assert predict([9, 10, 11, 12])==[0]
  assert predict([13, 14, 15, 16])==[1]
  assert predict([17, 18, 19, 20])==[0]
output: 
  def predict(new_features):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      data = pd.read_csv('dataset.csv')
      X = data[['Length', 'Width', 'Height', 'Weight']]
      y = data['Label']
  
      model = DecisionTreeClassifier()
      model.fit(X, y)
  
      return model.predict([new_features]).tolist()
  