input: Write a python function to read a CSV file into a pandas DataFrame and fit a RandomForestClassifier model using specified feature columns and a target column.
  assert model_function('data.csv', ['age', 'gender', 'hours_per_week', 'marital_status', 'education'], 'income').get_params()['n_estimators'] == 100
  assert isinstance(model_function('data.csv', ['age', 'gender', 'hours_per_week', 'marital_status', 'education'], 'income'), RandomForestClassifier)
  assert model_function('data.csv', ['age', 'gender', 'hours_per_week', 'marital_status', 'education'], 'income').n_classes_ == len(df['income'].unique())
output: import pandas as pd
  from sklearn.ensemble import RandomForestClassifier
  
  def model_function(csv_file, feature_columns, target_column):
      # Read CSV file into DataFrame
      df = pd.read_csv(csv_file)
      
      # Extract features and target
      X = df[feature_columns]
      y = df[target_column]
      
      # Initialize and fit the RandomForestClassifier
      model = RandomForestClassifier(n_estimators=100)
      model.fit(X, y)
      
      return model