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 dataset is loaded from a CSV file and split into features and label. A DecisionTreeClassifier is used to train a model on the data, and the function should return the predicted label for a given data point.
  assert predict([5.1, 3.5, 1.4, 0.2])==[0]
  assert predict([6.2, 3.4, 5.4, 2.3])==[1]
  assert predict([4.9, 3.0, 1.4, 0.2])==[0]
  assert predict([5.9, 3.0, 5.1, 1.8])==[1]
  assert predict([5.6, 3.0, 4.1, 1.3])==[1]
output: def predict(data_point):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      df = pd.read_csv('dataset.csv')
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Label']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict([data_point])