code: def __init__(
      self,
      penalty="l2",
      dual=False,
      tol=1e-4,
      C=1.0,
      fit_intercept=True,
      intercept_scaling=1,
      class_weight=None,
      random_state=None,
      solver="warn",
      max_iter=100,
      multi_class="warn",
      verbose=0,
      warm_start=False,
      n_jobs=None,
      l1_ratio=None,
  ):
      self.penalty = penalty
      self.dual = dual
      self.tol = tol
      self.C = C
      self.fit_intercept = fit_intercept
      self.intercept_scaling = intercept_scaling
      self.class_weight = class_weight
      self.random_state = random_state
      self.solver = solver
      self.max_iter = max_iter
      self.multi_class = multi_class
      self.verbose = verbose
      self.warm_start = warm_start
      self.n_jobs = n_jobs
      self.l1_ratio = l1_ratio
  
imports: import re
  from pprint import PrettyPrinter
  import numpy
  from sklearn.utils._pprint import _EstimatorPrettyPrinter
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.pipeline import make_pipeline
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest, chi2
  from sklearn import config_context
label_desc: Use image node_id 1 to create a new LogisticRegression object from inherited base classes: BaseEstimator with example: obj = LogisticRegression(penalty, dual, tol, C, fit_intercept, intercept_scaling, class_weight, random_state, solver, max_iter, multi_class, verbose, warm_start, n_jobs, l1_ratio)