input: Write a function called `train_decision_tree_classifier` that takes a CSV file path as input and trains a decision tree classifier to predict the fourth feature from the first three features. The function should return the trained classifier.
  
  assert train_decision_tree_classifier('data.csv').predict([[5, 3, 2]])==[1]
  assert train_decision_tree_classifier('data.csv').predict([[1, 1, 1]])==[0]
  assert train_decision_tree_classifier('data.csv').predict([[4, 2, 3]])==[1]
  assert train_decision_tree_classifier('data_empty.csv')==None
  assert train_decision_tree_classifier('data_missing_feature.csv')==None
output: 
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def train_decision_tree_classifier(csv_file_path):
      try:
          data = pd.read_csv(csv_file_path)
          if data.isnull().values.any():
              return None
          X = data.iloc[:, :3]
          y = data.iloc[:, 3]
          if X.empty or y.empty:
              return None
          clf = DecisionTreeClassifier()
          clf.fit(X, y)
          return clf
      except:
          return None
  