input: 
  def create_classification_model(data_path: str) -> RandomForestClassifier:
      """
      Load a dataset of customers from a given CSV file, create a classification model using 
      RandomForestClassifier in scikit-learn to determine customer churn, and return the trained model.
      The `churn` column in the dataset is used as the target variable.
  
      >>> create_classification_model('customer_data.csv').score(X_test, y_test)
      0.85  # Example accuracy score
      """
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # Load the data set
      data = pd.read_csv(data_path)
  
      # Select features
      X = data.drop(['churn'], axis=1)
  
      # Select targets
      y = data['churn']
  
      # Split the data 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 the model
      model = RandomForestClassifier()
  
      # Train the model
      model.fit(X_train, y_train)
  
      return model
  
output:     # Return the trained model
      return model