input: Write a function to predict the class labels of a given set of data. The function should load a dataset from a CSV file, split it into features (Length, Width, Height, Weight) and labels, train a DecisionTreeClassifier model on the data, and then predict the label for a new data point.
  assert predict([1, 2, 3, 4])==['label1']
  assert predict([5, 6, 7, 8])==['label2']
  assert predict([9, 10, 11, 12])==['label3']
output: def predict(new_data):
      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)
  
      prediction = model.predict([new_data])
      return prediction.tolist()