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. The function will load the dataset, split it into features and label, train a DecisionTreeClassifier model, and then predict the label for any given data.
  
  
  # Load the dataset
  import pandas as pd
  csv_url = 'http://test.test/test.csv'
  df = pd.read_csv(csv_url)
  
  # Split the dataset into features and label
  X = df.iloc[:, 0:4].values
  y = df.iloc[:, 4].values
  
  # Import the ML model
  from sklearn.tree import DecisionTreeClassifier
  
  # Create and fit the model
  model = DecisionTreeClassifier()
  model.fit(X, y)
  
  # Predict the label
  def predict(data):
      predicted_label = model.predict([data])
      return predicted_label
  
  # Test cases
  assert predict([1, 2, 3, 4]) == [some_label]
  assert predict([5, 6, 7, 8]) == [another_label]
  assert predict([9, 10, 11, 12]) == [yet_another_label]
  
output: def predict(data):
      predicted_label = model.predict([data])
      return predicted_label