class_docstr: Base class to implement randomized linear models for feature selection
  
  This implements the strategy by Meinshausen and Buhlman:
  stability selection with randomized sampling, and random re-weighting of
  the penalty.
docstr: Return the parameters passed to the estimator
code: def _make_estimator_and_params(self, X, y):
      """Return the parameters passed to the estimator"""
      raise NotImplementedError
  
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 3 for calling the BaseRandomizedLinearModel obj's underlying member method code with example usage: obj._make_estimator_and_params(X, y) without return types