code: def get_lambda_new_weights_and_labels(constraints, X, y, A):
      lambda_vec = get_lambda_vec(constraints, X, y, A)
      objective = constraints.default_objective()
      objective.load_data(X, y, sensitive_features=A)
      signed_weights = (
          objective.signed_weights()
          + constraints.signed_weights(lambda_vec)
      )
  
      if isinstance(constraints, LossMoment):
          redY = y
      else:  # classification
          redY = 1 * (signed_weights > 0)
      redW = signed_weights.abs()
      redW = y.shape[0] * redW / redW.sum()
      return lambda_vec, redW, redY
  
file_path: fairlearn/test/unit/reductions/exponentiated_gradient/test_lagrangian.py
imports: from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator
  from sklearn.dummy import DummyClassifier
  from fairlearn.reductions import BoundedGroupLoss, DemographicParity, EqualizedOdds, ErrorRate, ErrorRateParity, FalsePositiveRateParity, LossMoment, TruePositiveRateParity, ZeroOneLoss
  from fairlearn.reductions._exponentiated_gradient._lagrangian import _MESSAGE_BAD_OBJECTIVE, _Lagrangian
  from fairlearn.reductions._moments.bounded_group_loss import MeanLoss
  from .simple_learners import LeastSquaresBinaryClassifierLearner, MockEstimator
  from .test_utilities import _get_data
label_desc: Use image node_id 6 for calling a global function with example usage: get_lambda_new_weights_and_labels(constraints, X, y, A) and returns: lambda_vec,  redW,  redY