input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier trained on a dataset with 4 features: Length, Width, Height, and Weight. The dataset is loaded from a URL and split into features and labels. The function should return the predicted label for a single data point.
  
  predict([1.0, 2.0, 3.0, 4.0])==['ClassA']
  predict([5.0, 6.0, 7.0, 8.0])==['ClassB']
  predict([9.0, 10.0, 11.0, 12.0])==['ClassC']
  
output: import numpy as np
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(data_point):
      url = "https://example.com/dataset.csv"
      df = pd.read_csv(url)
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Label']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict(np.array(data_point).reshape(1, -1)).tolist()