docstr: initializer
  
  answers - a vector of answers to be returned by "predict"
  
  classes - a vector of class numbers to be used to populate classes_
docstr_tok: ["initializer","answers","-","a","vector","of","answers","to","be","returned","by","``","predict","''","classes","-","a","vector","of","class","numbers","to","be","used","to","populate","classes_"]
code: def __init__(self, answers, classes, proba):
      """initializer
  
      answers - a vector of answers to be returned by "predict"
  
      classes - a vector of class numbers to be used to populate classes_
      """
      self.answers_ = answers
      self.classes_ = classes
      self.proba_ = proba
  
imports: import contextlib
  import os
  import pickle
  import tempfile
  import numpy
  import pytest
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT, COLTYPE_INTEGER
  import cellprofiler.modules.classifyobjects
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.setting
  import cellprofiler_core.workspace
label_desc: Use image node_id 1 to create a new FakeClassifier object from inherited base classes: object with example: obj = FakeClassifier(answers, classes, proba)