code: def _randomized_lasso(
      X,
      y,
      weights,
      mask,
      alpha=1.0,
      verbose=False,
      precompute=False,
      eps=np.finfo(np.float).eps,
      max_iter=500,
  ):
      X = X[safe_mask(X, mask)]
      y = y[mask]
  
      # Center X and y to avoid fit the intercept
      X -= X.mean(axis=0)
      y -= y.mean()
  
      alpha = np.atleast_1d(np.asarray(alpha, dtype=np.float64))
  
      X = (1 - weights) * X
      with warnings.catch_warnings():
          warnings.simplefilter("ignore", ConvergenceWarning)
          alphas_, _, coef_ = lars_path(
              X,
              y,
              Gram=precompute,
              copy_X=False,
              copy_Gram=False,
              alpha_min=np.min(alpha),
              method="lasso",
              verbose=verbose,
              max_iter=max_iter,
              eps=eps,
          )
  
      if len(alpha) > 1:
          if len(alphas_) > 1:  # np.min(alpha) < alpha_min
              interpolator = interp1d(
                  alphas_[::-1],
                  coef_[:, ::-1],
                  bounds_error=False,
                  fill_value=0.0,
              )
              scores = interpolator(alpha) != 0.0
          else:
              scores = np.zeros((X.shape[1], len(alpha)), dtype=np.bool)
      else:
          scores = coef_[:, -1] != 0.0
      return scores
  
imports: import itertools
  from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy.sparse import issparse
  from scipy import sparse
  from scipy.interpolate import interp1d
  from .base import _preprocess_data
  from ..base import BaseEstimator, TransformerMixin
  from ..externals import six
  from ..externals.joblib import Memory, Parallel, delayed
  from ..utils import as_float_array, check_random_state, check_X_y, check_array, safe_mask
  from ..utils.validation import check_is_fitted
  from .least_angle import lars_path, LassoLarsIC
  from .logistic import LogisticRegression
  from ..exceptions import ConvergenceWarning
label_desc: Use image node_id 2 for calling a global function with example usage: _randomized_lasso(X, y, weights, mask, alpha, verbose, precompute, eps, max_iter) and returns: scores