code: def _predict_proba(lr, X):
      pred = safe_sparse_dot(X, lr.coef_.T)
      if hasattr(lr, "intercept_"):
          pred += lr.intercept_
      return softmax(pred)
  
file_path: scikit-learn/benchmarks/bench_saga.py
imports: import json
  import os
  import time
  import matplotlib.pyplot
  import numpy
  from sklearn.datasets import fetch_20newsgroups_vectorized, fetch_rcv1, load_digits, load_iris
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import log_loss
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelBinarizer, LabelEncoder
  from sklearn.utils.extmath import safe_sparse_dot, softmax
  from sklearn.utils.parallel import Parallel, delayed
label_desc: Use image node_id 2 for calling a global function with example usage: _predict_proba(lr, X) and returns: softmax