code: def _make_dataset(X, y, split=False):
      if split:
          X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)
          return Dataset(Split(X_train, X_test), Split(y_train, y_test))
      return Dataset(X, y)
  
imports: import os
  import argparse
  import numpy
  import pandas
  import matplotlib.pyplot
  from yellowbrick.features import Rank2D, RadViz
  from yellowbrick.model_selection import LearningCurve
  from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
  from yellowbrick.classifier import ClassificationReport, DiscriminationThreshold
  from yellowbrick.regressor import ResidualsPlot, PredictionError, AlphaSelection
  from collections import namedtuple
  from sklearn.datasets import make_blobs
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.cluster import MiniBatchKMeans, Birch
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LassoCV, RidgeCV, LogisticRegression
label_desc: Use image node_id 1 for calling a global function with example usage: _make_dataset(X, y, split) and returns: Dataset, Dataset