input: def predict_label(data):
      """ Predict the class label of a given set of data using a fitted DecisionTreeClassifier.
      The dataset contains 4 features: Length, Width, Height, and Weight.
      >>> predict_label([5.0, 2.0, 3.0, 1.0])
      ['class_A']
      >>> predict_label([1.0, 3.0, 2.0, 0.5])
      ['class_B']
      """
      # 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
output:     # Predict the label
      predicted_label = model.predict([data])
      return predicted_label