code: def load_data(dtype=np.float32, order="C", random_state=13):
      """Load the data, then cache and memmap the train/test split"""
      ######################################################################
      # Load dataset
      print("Loading dataset...")
      data = fetch_covtype(
          download_if_missing=True,
          shuffle=True,
          random_state=random_state,
      )
      X = check_array(data["data"], dtype=dtype, order=order)
      y = (data["target"] != 1).astype(int)
  
      # Create train-test split (as [Joachims, 2006])
      print("Creating train-test split...")
      n_train = 522911
      X_train = X[:n_train]
      y_train = y[:n_train]
      X_test = X[n_train:]
      y_test = y[n_train:]
  
      # Standardize first 10 features (the numerical ones)
      mean = X_train.mean(axis=0)
      std = X_train.std(axis=0)
      mean[10:] = 0.0
      std[10:] = 1.0
      X_train = (X_train - mean) / std
      X_test = (X_test - mean) / std
      return X_train, X_test, y_train, y_test
  
file_path: scikit-learn/benchmarks/bench_covertype.py
imports: import argparse
  import os
  from time import time
  import numpy
  from joblib import Memory
  from sklearn.datasets import fetch_covtype, get_data_home
  from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, SGDClassifier
  from sklearn.metrics import zero_one_loss
  from sklearn.naive_bayes import GaussianNB
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import check_array
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(dtype, order, random_state) and returns: X_train,  X_test,  y_train,  y_test