input: Write a function to predict the class labels of a given set of data based on its features: Length, Width, Height, and Weight. Load the dataset from a given URL, split it into features and labels, fit a DecisionTreeClassifier model on the data, and then use the model to predict labels for new data points.
  
  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([1.0, 2.0, 3.0, 4.0])) in ["class1"]
  assert (predict([2.0, 3.0, 4.0, 5.0])) in ["class2"]
  assert (predict([3.0, 4.0, 5.0, 6.0])) in ["class3"]
  assert (predict([4.0, 5.0, 6.0, 7.0])) in ["class1"]
  assert (predict([5.0, 6.0, 7.0, 8.0])) in ["class2"]
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