code: def test_skip_num_points_gradient(csr_container):
      # Test the kwargs option skip_num_points.
      #
      # Skip num points should make it such that the Barnes_hut gradient
      # is not calculated for indices below skip_num_point.
      # Aside from skip_num_points=2 and the first two gradient rows
      # being set to zero, these data points are the same as in
      # test_answer_gradient_four_points()
      pos_input = np.array(
          [[1.0, 0.0], [0.0, 1.0], [5.0, 2.0], [7.3, 2.2]]
      )
      pos_output = np.array(
          [
              [6.080564e-05, -7.120823e-05],
              [-1.718945e-04, -4.000536e-05],
              [-2.271720e-04, 8.663310e-05],
              [-1.032577e-04, -3.582033e-05],
          ]
      )
      neighbors = np.array([[1, 2, 3], [0, 2, 3], [1, 0, 3], [1, 2, 0]])
      grad_output = np.array(
          [
              [0.0, 0.0],
              [0.0, 0.0],
              [4.24275173e-08, -3.69569698e-08],
              [-2.58720939e-09, 7.52706374e-09],
          ]
      )
      _run_answer_test(
          pos_input,
          pos_output,
          neighbors,
          grad_output,
          csr_container,
          False,
          0.1,
          2,
      )
  
imports: import sys
  from io import StringIO
  import numpy
  import pytest
  import scipy.sparse
  from numpy.testing import assert_allclose
  from scipy.optimize import check_grad
  from scipy.spatial.distance import pdist, squareform
  from sklearn import config_context
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import EfficiencyWarning
  from sklearn.manifold import TSNE, _barnes_hut_tsne
  from sklearn.manifold._t_sne import _gradient_descent, _joint_probabilities, _joint_probabilities_nn, _kl_divergence, _kl_divergence_bh, trustworthiness
  from sklearn.manifold._utils import _binary_search_perplexity
  from sklearn.metrics.pairwise import cosine_distances, manhattan_distances, pairwise_distances
  from sklearn.neighbors import NearestNeighbors, kneighbors_graph
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings, skip_if_32bit
  from sklearn.utils.fixes import CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 28 for calling a global function with example usage: test_skip_num_points_gradient(csr_container) without return types