input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier. The dataset contains 4 features: Length, Width, Height, and Weight. Load the dataset from a CSV file and split it into features and labels. Train a DecisionTreeClassifier on the dataset and use it to predict the class label for a new data point.
  
  assert predict([1.0, 2.0, 3.0, 4.0]) == [0]
  assert predict([10.0, 20.0, 30.0, 40.0]) == [1]
  assert predict([5.0, 5.0, 5.0, 5.0]) == [2]
  assert predict([0.1, 0.2, 0.3, 0.4]) == [0]
  assert predict([0.9, 1.8, 2.7, 3.6]) == [1]
output: def predict(new_data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      df = pd.read_csv('dataset.csv')
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Label']
  
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
  
      return clf.predict([new_data]).tolist()