input: Write a function to predict the class labels of a given set of data. The dataset contains 4 features: Length, Width, Height, and Weight. If the dataset is loaded from 'http://test.test/test.csv' and a DecisionTreeClassifier is used to fit the model, return the predicted label for a given data point.
  assert predict([1, 2, 3, 4])==['A']
  assert predict([5, 6, 7, 8])==['B']
  assert predict([9, 10, 11, 12])==['A']
  assert predict([13, 14, 15, 16])==['B']
  assert predict([17, 18, 19, 20])==['A']
output: def predict(data_point):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      df = pd.read_csv('http://test.test/test.csv')
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Label']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict([data_point]).tolist()