input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. Implement the classifier using the scikit-learn library and fit it to a dataset containing features A, B, C, and a target feature. If the dataset is successfully fitted, return True. If there is an error during the process, return False. If the dataset is empty, return False.
  
  assert decision_tree_classifier('data.csv')==True
  assert decision_tree_classifier('empty_data.csv')==False
  assert decision_tree_classifier('invalid_data.csv')==False
output: def decision_tree_classifier(filename):
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          from sklearn.model_selection import train_test_split
          
          data = pd.read_csv(filename)
          if data.empty:
              return False
          
          X = data[['A', 'B', 'C']]
          y = data['target']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          clf = DecisionTreeClassifier()
          clf.fit(X_train, y_train)
          
          return True
      except:
          return False