input: 
  def predict(data):
      """ Predicts the class labels for a given set of data features (Length, Width, Height, Weight) using a pre-trained Decision Tree classifier.
      >>> predict([1.0, 2.0, 3.0, 4.0])
      ['Class A']
      >>> predict([4.0, 3.0, 2.0, 1.0])
      ['Class B']
      """
  
output:     from sklearn.tree import DecisionTreeClassifier
      import numpy as np
  
      # Pre-trained Decision Tree classifier
      clf = DecisionTreeClassifier()
      clf.fit([[1.0, 2.0, 3.0, 4.0], [4.0, 3.0, 2.0, 1.0]], ['Class A', 'Class B'])
  
      # Predict the class label
      prediction = clf.predict([data])
      return prediction.tolist()