code: def generate_classification_data():
      data_inputs, expected_outputs = make_classification(
          n_samples=10000,
          n_repeated=0,
          n_classes=3,
          n_features=4,
          n_clusters_per_class=1,
          class_sep=1.5,
          flip_y=0,
          weights=[0.5, 0.5, 0.5],
      )
  
      X_train, X_test, y_train, y_test = train_test_split(
          data_inputs, expected_outputs, test_size=0.20
      )
  
      return X_train, y_train, X_test, y_test
  
imports: import shutil
  from neuraxle.base import ExecutionContext
  from neuraxle.hyperparams.distributions import Boolean, Choice, LogUniform, RandInt
  from neuraxle.hyperparams.space import HyperparameterSpace
  from neuraxle.metaopt.auto_ml import AutoML, RandomSearchSampler, ValidationSplitter
  from neuraxle.metaopt.callbacks import ScoringCallback
  from neuraxle.metaopt.repositories.json import HyperparamsOnDiskRepository
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.flow import ChooseOneStepOf
  from neuraxle.steps.numpy import NumpyRavel
  from neuraxle.steps.output_handlers import OutputTransformerWrapper
  from neuraxle.steps.sklearn import SKLearnWrapper
  from sklearn.datasets import make_classification
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, RidgeClassifier
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: generate_classification_data() and returns: X_train,  y_train,  X_test,  y_test