code: def target_analysis_binary():
      kwargs = {
          "n_samples": 100,
          "n_classes": 2,
          "n_features": 20,
          "n_informative": 8,
          "n_redundant": 2,
          "n_clusters_per_class": 1,
          "random_state": 89092,
      }
  
      X, y = datasets.make_classification(**kwargs)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, random_state=101
      )
      return X_train, X_test, y_train, y_test
  
imports: from copy import copy
  import sys
  import os
  from pathlib import Path
  import numpy
  from sklearn.linear_model import LogisticRegression
  import pytest
  from matplotlib.testing.conftest import mpl_test_settings, mpl_image_comparison_parameters
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import GridSearchCV
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 10 for calling a global function with example usage: target_analysis_binary() and returns: X_train,  X_test,  y_train,  y_test