input: Write a function to train an IsolationForest model to detect fraud in transaction data.
  assert isinstance(train_isolationforest_model('transaction_data.csv'), IsolationForest)
  assert train_isolationforest_model('transaction_data.csv').n_estimators == 100
output: def train_isolationforest_model(data_path):
      import pandas as pd
      from sklearn.ensemble import IsolationForest
  
      # Load the transaction data
      df = pd.read_csv(data_path)
      
      # Assuming that the transaction data has a column 'transaction_amount'
      X = df[['transaction_amount']]
  
      # Train the IsolationForest model
      model = IsolationForest(n_estimators=100, contamination='auto', random_state=42)
      model.fit(X)
      
      return model