code: def test_add_features_does_not_fail_if_initial_dataset_has_zero_informative_features(
      capsys,
  ):
      arr_a = np.zeros((100, 1), dtype=np.float32)
      arr_b = np.random.normal(size=(100, 5))
  
      dataset_a = lgb.Dataset(arr_a).construct()
      expected_msg = (
          "[LightGBM] [Warning] There are no meaningful features which satisfy "
          "the provided configuration. Decreasing Dataset parameters min_data_in_bin "
          "or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n"
      )
      log_lines = capsys.readouterr().out
      assert expected_msg in log_lines
  
      dataset_b = lgb.Dataset(arr_b).construct()
  
      original_handle = dataset_a._handle.value
      dataset_a.add_features_from(dataset_b)
      assert dataset_a.num_feature() == 6
      assert dataset_a.num_data() == 100
      assert dataset_a._handle.value == original_handle
  
imports: import filecmp
  import numbers
  import re
  from copy import deepcopy
  from os import getenv
  from pathlib import Path
  import numpy
  import pytest
  from scipy import sparse
  from sklearn.datasets import dump_svmlight_file, load_svmlight_file
  from sklearn.model_selection import train_test_split
  import lightgbm
  from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
  from .utils import dummy_obj, load_breast_cancer, mse_obj, np_assert_array_equal
label_desc: Use image node_id 14 for calling a global function with example usage: test_add_features_does_not_fail_if_initial_dataset_has_zero_informative_features(capsys) without return types