code: def log_reg_probs(new_adult):
      """Train a LogisticRegression model and return predicted probabilities."""
      X, y, sample_weight = new_adult
      lr = LogisticRegression(solver="lbfgs", max_iter=500)
      lr.fit(X, y, sample_weight=sample_weight)
      return lr.predict_proba(X)
  
imports: from itertools import product
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import make_scorer
  from aif360.algorithms.postprocessing import RejectOptionClassification
  from aif360.sklearn.postprocessing import RejectOptionClassifier, RejectOptionClassifierCV, PostProcessingMeta
  from aif360.sklearn.metrics import generalized_entropy_error
label_desc: Use image node_id 1 for calling a global function with example usage: log_reg_probs(new_adult) and returns: lr