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 should load the dataset from a specified URL, split the dataset into features and label, train a DecisionTreeClassifier on the dataset, and then predict the label for a new data point.
  
  def predict(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
      predicted_label = model.predict([data])
      return predicted_label
  
  assert predict([1, 2, 3, 4]) == [0]
  assert predict([5, 4, 7, 8]) == [1]
  assert predict([2, 1, 6, 3]) == [0]
  assert predict([8, 9, 10, 5]) == [1]
  assert predict([3, 2, 1, 4]) == [0]
output: def predict(data):
      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)
      predicted_label = model.predict([data])
      return predicted_label