code: def get_500_classes_datamanager():
      weights = ([0.002] * 475) + ([0.001] * 25)
      X, Y = sklearn.datasets.make_classification(
          n_samples=1000,
          n_features=20,
          n_classes=500,
          n_clusters_per_class=1,
          n_informative=15,
          n_redundant=5,
          n_repeated=0,
          weights=weights,
          flip_y=0,
          class_sep=1.0,
          hypercube=True,
          shift=None,
          scale=1.0,
          shuffle=True,
          random_state=1,
      )
  
      D = Dummy()
      D.info = {
          "task": MULTICLASS_CLASSIFICATION,
          "is_sparse": False,
          "label_num": 500,
      }
      D.data = {
          "X_train": X[:700],
          "Y_train": Y[:700],
          "X_test": X[710:],
          "Y_test": Y[710:],
      }
      D.feat_type = {i: "numerical" for i in range(20)}
      return D
  
imports: import functools
  import tempfile
  import traceback
  import numpy
  import sklearn.datasets
  import sklearn.model_selection
  from numpy.linalg import LinAlgError
  from sklearn import preprocessing
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, REGRESSION
  from autosklearn.data.xy_data_manager import XYDataManager
  from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, f1_micro, f1_weighted, log_loss, precision_macro, precision_micro, precision_weighted, recall_macro, recall_micro, recall_weighted
  from autosklearn.pipeline.util import get_dataset
  from autosklearn.util.data import convert_to_bin
  import unittest
label_desc: Use image node_id 7 for calling a global function with example usage: get_500_classes_datamanager() and returns: D