input: from typing import List, Any
  
  def predict_class_labels(data: List[List[float]]) -> List[Any]:
      """Predicts the class labels for a given set of data with features: Length, Width, Height, and Weight.
      Loads the dataset from a CSV file, splits it into features and labels, trains a DecisionTreeClassifier
      on the dataset, and then uses this trained model to make predictions for new data.
      
      >>> predict_class_labels([[7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 5.3, 2.3]])
      ['class1', 'class2']
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
  
      # Load the dataset from a CSV file
      df = pd.read_csv('dataset.csv')
  
      # Split the dataset into features and labels
      X = df[['Length', 'Width', 'Height', 'Weight']]
      y = df['Class']
  
      # Train a DecisionTreeClassifier
      model = DecisionTreeClassifier()
      model.fit(X, y)
  
      # Predict the class labels for the given data
      predictions = model.predict(data)
  
      return predictions.tolist()