text: 
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import TheilSenRegressor
  import scipy.stats as ss
  
  model = Pipeline(steps=[
      ('scl', StandardScaler()),
      ('lin', TheilSenRegressor(
          fit_intercept = True,
          copy_X = True,
          max_subpopulation = 10000,  # constrain 'n choose k' to 10k
          n_subsamples = None,  # maximal robustness
          max_iter = 300,
          tol = 1e-3,
          random_state = 42
      ))
  ])
  
  hyper = {
      # 'lin__epsilon': ss.uniform(1.0, 6.0),  # threshold for standardized error
      # 'lin__alpha': ss.gamma(a=1.5, loc=1e-5, scale=.7),  # alpha ~ [0.001, 10]
  }
  
  meta = {
      'id': "simi16",
      'name': 'LinReg Theil-Sen',
      'descriptions': (
          "Theil-Sen Regression, standard-normal transformed features."),
      'solver': 'L-BFGS',
      'active': True,
      'keywords': [
          'robust regression', 'univariate regression', 'multiple regression',
          'theil-sen regression', 'sklearn.linear_model.TheilSenRegressor',
          'theil-sen estimator', 'sen slope estimator', 'slope selection',
          'single-median method', 'kendall robust line-fit method',
          'kendall–theil robust line'],
      'output_num': 'single',
      'output_scale': 'interval',
      'output_dtype': 'float',
      'input_num': 'multi',
      'input_scale': 'interval',
      'input_dtype': 'float'
  }
  