code: def get_regression_datamanager():
      X_train, Y_train, X_test, Y_test = get_dataset("boston")
      indices = list(range(X_train.shape[0]))
      np.random.seed(1)
      np.random.shuffle(indices)
      X_train = X_train[indices]
      Y_train = Y_train[indices]
  
      X_test = X_test[200:]
      Y_test = Y_test[200:]
  
      D = Dummy()
      D.info = {"task": REGRESSION, "is_sparse": False, "label_num": 1}
      D.data = {
          "X_train": X_train,
          "Y_train": Y_train.reshape((-1, 1)),
          "X_test": X_test,
          "Y_test": Y_test.reshape((-1, 1)),
      }
      D.feat_type = {i: "numerical" for i in range(X_train.shape[1])}
      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 6 for calling a global function with example usage: get_regression_datamanager() and returns: D