input: Write a python function to load a dataset from a CSV file, split it into training and test sets, train a Decision Tree classifier, and return the accuracy of the model.
  assert abs(classify_and_evaluate('flag.data') - 0.65) < 0.1
  assert abs(classify_and_evaluate('another_dataset.csv') - 0.75) < 0.1
output: def classify_and_evaluate(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(csv_file)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train a Decision Tree classifier
      clf = DecisionTreeClassifier(random_state=42)
      clf.fit(X_train, y_train)
  
      # Make predictions and calculate accuracy
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy