code: def monotonic2():
      """
      Dataset with monotonic constraints.
      Can be used for regression.
      The first column contains target values.
      Other columns contain contain numerical features, for which monotonic constraints must hold.
  
      For features in columns named MonotonicNeg*, if feature value decreases, then prediction
      value must not decrease. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic negative feature MNeg, such that x1[MNeg] > x2[MNeg], then
      the following inequality must hold for predictions: f(x1) <= f(x2)
      For features in columns named MonotonicPos*, if feature value decreases, then prediction
      value must not increase. Thus, if there are two samples x1, x2 with all features being
      equal except for a monotonic positive feature MPos, such that x1[MPos] > x2[MPos],
      then the following inequality must hold for predictions: f(x1) >= f(x2)
      """
      url = "https://storage.mds.yandex.net/get-devtools-opensource/250854/monotonic2.tar.gz"
      md5 = "ce559e212cb72c156269f6f9a641baca"
      dataset_name, train_file, test_file = (
          "monotonic2",
          "train.tsv",
          "test.tsv",
      )
      return _load_dataset_pd(
          url, md5, dataset_name, train_file, test_file, sep="\t"
      )
  
imports: import hashlib
  import logging
  import numpy
  import os
  import pandas
  import tarfile
  import tempfile
  import six
  import shutil
  from .core import PATH_TYPES, fspath
label_desc: Use image node_id 18 for calling a global function with example usage: monotonic2() and returns: _load_dataset_pd