input: Write a function to create and train a decision tree classifier using stock data from a CSV file. The function should split the data into training and testing sets, train the model, and print the model's accuracy. The CSV file name should be provided as an input argument to the function.
  
  def construct_decision_tree(csv_file: str) -> None:
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(csv_file)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      print('Model accuracy: ', accuracy_score(y_test, y_pred))
  
  assert construct_decision_tree("stockdata.csv") is None
  assert construct_decision_tree("stockdata_empty.csv") is None
output: def construct_decision_tree(csv_file: str) -> None:
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(csv_file)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      print('Model accuracy: ', accuracy_score(y_test, y_pred))