input: Write a function to predict the class labels of a given set of data using a Decision Tree Classifier. The dataset contains 4 features: Length, Width, Height, and Weight. Load the dataset from a CSV file, split it into features and labels, train a Decision Tree Classifier on the dataset, and then use the trained model to predict the class label for a new data point.
  import pandas as pd
  csv_url = 'http://test.test/test.csv'
  df = pd.read_csv(csv_url)
  X = df.iloc[:, 0:4].values
  y = df.iloc[:, 4].values
  from sklearn.tree import DecisionTreeClassifier
  model = DecisionTreeClassifier()
  model.fit(X, y)
  def predict(data):
      predicted_label = model.predict([data])
      return predicted_label
  assert predict([10, 20, 30, 40])==[1]
  assert predict([15, 25, 35, 45])==[0]
  assert predict([5, 15, 25, 35])==[2]
output: def predict(data):
      predicted_label = model.predict([data])
      return predicted_label