input: Write a python function to train a RandomForestClassifier using specific features and a target label from a dataframe.
  assert train_random_forest(df, ['Gender', 'Income'], 'Label').n_estimators == 100
  assert train_random_forest(df, ['Gender', 'Income'], 'Label').max_depth == 2
  assert train_random_forest(df, ['Gender', 'Income'], 'Label').random_state == 0
output: def train_random_forest(df, features, target):
      from sklearn.ensemble import RandomForestClassifier
      
      X = df[features]
      y = df[target]
      
      model = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)
      model.fit(X, y)
      
      return model