code: def _lasso_stability_path(X, y, mask, weights, eps):
      "Inner loop of lasso_stability_path"
      X = X * weights[np.newaxis, :]
      X = X[safe_mask(X, mask), :]
      y = y[mask]
  
      alpha_max = np.max(np.abs(np.dot(X.T, y))) / X.shape[0]
      alpha_min = eps * alpha_max  # set for early stopping in path
      with warnings.catch_warnings():
          warnings.simplefilter("ignore", ConvergenceWarning)
          alphas, _, coefs = lars_path(
              X, y, method="lasso", verbose=False, alpha_min=alpha_min
          )
      # Scale alpha by alpha_max
      alphas /= alphas[0]
      # Sort alphas in ascending order
      alphas = alphas[::-1]
      coefs = coefs[:, ::-1]
      # Get rid of the alphas that are too small
      mask = alphas >= eps
      # We also want to keep the first one: it should be close to the OLS
      # solution
      mask[0] = True
      alphas = alphas[mask]
      coefs = coefs[:, mask]
      return alphas, coefs
  
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 4 for calling a global function with example usage: _lasso_stability_path(X, y, mask, weights, eps) and returns: alphas,  coefs