code: def main():
      # parse command-line arguments
      args = parse_args()
  
      # prepare train and test data
      # Ordinary Least Squares (OLS)
      np.random.seed(9876789)
      nsamples = 100
      x = np.linspace(0, 10, 100)
      X = np.column_stack((x, x**2))
      beta = np.array([1, 0.1, 10])
      e = np.random.normal(size=nsamples)
      X = sm.add_constant(X)
      y = np.dot(X, beta) + e
  
      # enable auto logging
      mlflow.statsmodels.autolog()
  
      with mlflow.start_run():
          ols = sm.OLS(y, X)
          model = ols.fit(method=args.inverse_method)
  
          # evaluate model
          y_pred = model.predict(X)
          mse = mean_squared_error(y, y_pred)
  
          # log metrics
          mlflow.log_metrics({"mse": mse})
  
imports: import argparse
  import numpy
  import statsmodels.api
  from sklearn.metrics import mean_squared_error
  import mlflow
  import mlflow.statsmodels
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types