code: def test_numpy_feature_names(self):
      def verify_numpy_feature_names():
          world_size = xgb.collective.get_world_size()
          data = np.random.randn(5, 5)
          feature_names = [f"feature{x}" for x in range(5)]
          feature_types = ["float"] * 5
          dm = xgb.DMatrix(
              data,
              feature_names=feature_names,
              feature_types=feature_types,
              data_split_mode=DataSplitMode.COL,
          )
          assert dm.num_row() == 5
          assert dm.num_col() == 5 * world_size
          assert len(dm.feature_names) == 5 * world_size
          assert dm.feature_names == tm.column_split_feature_names(
              feature_names, world_size
          )
          assert len(dm.feature_types) == 5 * world_size
          assert dm.feature_types == ["float"] * 5 * world_size
  
      tm.run_with_rabit(
          world_size=3, test_fn=verify_numpy_feature_names
      )
  
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 2 for calling the TestDMatrixColumnSplit obj's underlying member method code with example usage: obj.test_numpy_feature_names() without return types