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. The function should load the dataset from a CSV file, split the dataset into features and label, train a DecisionTreeClassifier, and then return the predicted label for a given input data.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([5, 6, 7, 8])==[1]
  assert predict([9, 10, 11, 12])==[0]
  assert predict([13, 14, 15, 16])==[1]
  assert predict([17, 18, 19, 20])==[0]
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(input_data):
      df = pd.read_csv('dataset.csv')
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Label']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict([input_data])