input: Write a python function to build a decision tree classifier for a given dataset and return its train and test accuracy.
  assert build_decision_tree_classifier('movie_data.csv') == (0.95, 0.85)
  assert build_decision_tree_classifier('other_movie_data.csv') == (0.90, 0.80)
output: def build_decision_tree_classifier(file_path):
      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
      df = pd.read_csv(file_path)
  
      # Assume the last column is the target variable
      X = df.iloc[:, :-1]
      y = df.iloc[:, -1]
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create a decision tree classifier
      clf = DecisionTreeClassifier()
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Make predictions
      y_train_pred = clf.predict(X_train)
      y_test_pred = clf.predict(X_test)
  
      # Calculate accuracy
      train_accuracy = accuracy_score(y_train, y_train_pred)
      test_accuracy = accuracy_score(y_test, y_test_pred)
  
      return train_accuracy, test_accuracy