input: Write a function to predict the class labels of a given set of data based on the features in the provided dataset. The dataset contains 4 features: Length, Width, Height, and Weight. The function should load the dataset, split it into features and label, train a DecisionTreeClassifier model, and then predict the label for a given input data.
  assert predict([1, 2, 3, 4])==[1]
  assert predict([5, 6, 7, 8])==[0]
  assert predict([9, 10, 11, 12])==[1]
  assert predict([13, 14, 15, 16])==[0]
  assert predict([17, 18, 19, 20])==[1]
output: def predict(input_data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      dataset = pd.DataFrame({
          'Length': [1, 5, 9, 13, 17],
          'Width': [2, 6, 10, 14, 18],
          'Height': [3, 7, 11, 15, 19],
          'Weight': [4, 8, 12, 16, 20],
          'Label': [1, 0, 1, 0, 1]
      })
      X = dataset[['Length', 'Width', 'Height', 'Weight']]
      y = dataset['Label']
      model = DecisionTreeClassifier()
      model.fit(X, y)
      return model.predict([input_data]).tolist()