code: def iris_random_forest_single_class(iris):
      """Return trained RandomForestClassifier on iris data modified to a binary label."""
      clf = RandomForestClassifier(random_state=0)
      idx = iris.target != 2
      features = iris.drop("target", axis=1)[idx]
      target = iris.target[idx]
      clf.fit(features, target)
      return clf
  
imports: import logging
  import random
  from typing import Tuple
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_diabetes, load_iris
  from sklearn.ensemble import AdaBoostClassifier, GradientBoostingRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import KBinsDiscretizer
  from deepchecks.core.check_result import CheckResult
  from deepchecks.tabular import Context, Dataset, TrainTestCheck
  from deepchecks.tabular.datasets.classification import lending_club
  from deepchecks.utils.logger import set_verbosity
label_desc: Use image node_id 13 for calling a global function with example usage: iris_random_forest_single_class(iris) and returns: clf