input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier model trained on a dataset with 4 features: Length, Width, Height, and Weight. The model should be trained using a URL pointing to a CSV file, where the first four columns represent the features and the fifth column represents the labels.
  assert predict([12.0, 4.5, 7.8, 340.2])==['Class A']
  assert predict([11.0, 5.5, 6.8, 300.0])==['Class B']
  assert predict([14.0, 3.5, 9.8, 360.0])==['Class C']
  assert predict([10.0, 4.0, 6.0, 280.0])==['Class B']
  assert predict([13.0, 5.0, 8.0, 320.0])==['Class A']
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(data):
      url = "https://example.com/data.csv"
      df = pd.read_csv(url)
      X = df.iloc[:, :4]
      y = df.iloc[:, 4]
      model = DecisionTreeClassifier()
      model.fit(X, y)
      return model.predict([data]).tolist()