code: def get_abalone_datamanager():
      # https://www.openml.org/d/183
      dataset_name = "abalone"
      data = sklearn.datasets.fetch_openml(data_id=183, as_frame=True)
      feat_type = {
          i: "Categorical" if x.name == "category" else "Numerical"
          for i, x in enumerate(data["data"].dtypes)
      }
      X, y = sklearn.datasets.fetch_openml(
          data_id=183, return_X_y=True, as_frame=False
      )
      y = preprocessing.LabelEncoder().fit_transform(y)
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = sklearn.model_selection.train_test_split(X, y, random_state=1)
  
      D = XYDataManager(
          X_train,
          y_train,
          X_test,
          y_test,
          MULTICLASS_CLASSIFICATION,
          feat_type,
          dataset_name,
      )
      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 3 for calling a global function with example usage: get_abalone_datamanager() and returns: D