code: def __init__(
      self,
      penalty="l2",
      dual=False,
      tol=1e-4,
      C=1.0,
      fit_intercept=True,
      intercept_scaling=1.0,
      class_weight=None,
      random_state=None,
      solver="admm",
      max_iter=100,
      multi_class="ovr",
      verbose=0,
      warm_start=False,
      n_jobs=1,
      solver_kwargs=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.solver_kwargs = solver_kwargs
  
imports: import textwrap
  from dask_glm import algorithms, families
  from dask_glm.utils import accuracy_score, add_intercept, dot, exp, poisson_deviance, sigmoid
  from sklearn.base import BaseEstimator
  from ..metrics import r2_score
  from ..utils import check_array
  from .utils import lr_prob_stack
label_desc: Use image node_id 2 to create a new _GLM object from inherited base classes: BaseEstimator with example: obj = _GLM(penalty, dual, tol, C, fit_intercept, intercept_scaling, class_weight, random_state, solver, max_iter, multi_class, verbose, warm_start, n_jobs, solver_kwargs)