class_docstr: For tests related to N-dim input.
  Estimator accepts N-dim array and reshape it to 2-dim array
class_docstr_tok: ["For","tests","related","to","N-dim","input",".","Estimator","accepts","N-dim","array","and","reshape","it","to","2-dim","array"]
code: def predict_proba(self, X):
      return super(LogisticRegressionUnrolled, self).predict_proba(
          X.reshape(X.shape[0], -1)
      )
  
imports: from __future__ import print_function
  from __future__ import division
  import unittest
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  import os
  import glob
  import numpy
  import scipy.stats
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import StratifiedKFold
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import log_loss
  from sklearn.metrics import make_scorer
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from vecstack import stacking
label_desc: Use image node_id 3 for calling the LogisticRegressionUnrolled obj's underlying member method code with example usage: obj.predict_proba(X) and returns: super