code: def monotonic1():
      """
      Dataset with monotonic constraints.
      Can be used for poisson regression.
      Has several numerical and several categorical features.
      The first column contains target values. Columns with names Cat* contain categorical features.
      Columns with names Num* contain numerical features.
  
      Dataset also contains several 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 M, such that x1[M] > x2[M], then the following inequality must
      hold for predictions: f(x1) <= f(x2)
      """
      url = "https://storage.mds.yandex.net/get-devtools-opensource/479623/monotonic1.tar.gz"
      md5 = "1b9d8e15bc3fd6f1498e652e7fc4f4ca"
      dataset_name, train_file, test_file = (
          "monotonic1",
          "train.tsv",
          "test.tsv",
      )
      return _load_dataset_pd(
          url,
          md5,
          dataset_name,
          train_file,
          test_file,
          sep="\t",
          cache=True,
      )
  
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 17 for calling a global function with example usage: monotonic1() and returns: _load_dataset_pd