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. Return the predicted class label.
  
  def predict(data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label
  
  assert predict([5, 3, 2, 0.5])==[0]
  assert predict([4, 4, 3, 1.2])==[1]
  assert predict([6, 2, 2, 0.8])==[0]
output: def predict(data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label