input: Write a function to implement a decision tree classifier in Python that predicts a fourth feature from three other features. The function should read a dataset from a CSV file, split it into features and labels, train the classifier on the data, and return the trained classifier. If the dataset is empty, the function should return None.
  assert implement_decision_tree_classifier('data.csv') != None
  assert implement_decision_tree_classifier('empty_data.csv') == None
  assert implement_decision_tree_classifier('data_with_invalid_rows.csv') != None
  assert implement_decision_tree_classifier('data_with_missing_values.csv') != None
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.model_selection import train_test_split
  
  def implement_decision_tree_classifier(file_path):
      try:
          data = pd.read_csv(file_path)
          if data.empty:
              return None
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          clf = DecisionTreeClassifier()
          clf.fit(X, y)
          return clf
      except Exception as e:
          return None