code: def test_feature_names_slice(self):
      data = np.random.randn(5, 5)
  
      # different length
      with pytest.raises(ValueError):
          xgb.DMatrix(data, feature_names=list("abcdef"))
      # contains duplicates
      with pytest.raises(ValueError):
          xgb.DMatrix(data, feature_names=["a", "b", "c", "d", "d"])
      # contains symbol
      with pytest.raises(ValueError):
          xgb.DMatrix(data, feature_names=["a", "b", "c", "d", "e<1"])
  
      dm = xgb.DMatrix(data)
      dm.feature_names = list("abcde")
      assert dm.feature_names == list("abcde")
  
      assert dm.slice([0, 1]).num_col() == dm.num_col()
      assert dm.slice([0, 1]).feature_names == dm.feature_names
  
      with pytest.raises(
          ValueError, match=r"Duplicates found: \['bar'\]"
      ):
          dm.feature_names = ["bar"] * (data.shape[1] - 2) + ["a", "b"]
  
      dm.feature_types = list("qiqiq")
      assert dm.feature_types == list("qiqiq")
  
      with pytest.raises(ValueError):
          dm.feature_types = list("abcde")
  
      # reset
      dm.feature_names = None
      dm.feature_types = None
      assert dm.feature_names is None
      assert dm.feature_types is None
  
imports: import csv
  import os
  import tempfile
  import warnings
  import numpy
  import pytest
  import scipy.sparse
  from hypothesis import given, settings, strategies
  from scipy.sparse import csr_matrix, rand
  import xgboost
  from xgboost import testing
  from xgboost.core import DataSplitMode
  from xgboost.testing.data import np_dtypes, run_base_margin_info
label_desc: Use image node_id 5 for calling the TestDMatrix obj's underlying member method code with example usage: obj.test_feature_names_slice() without return types