code: def test_target_line_based_out_of_bounds():
      test_cases = [
          (np.array([[-10, 0, 0], [0, 0, 0]]), 0),
          (np.array([[-10, 0, 0], [1, -10, 0]]), 0),
          (np.array([[0, 0, 0], [10, 10, 10]]), 0),
          (np.array([[-2, -0.6, -0.6], [2, -0.6, -0.6]]), 0),
          (np.array([[-10000, 0, 0], [0, 0, 0]]), 0),
          (np.array([[-10, 0, 0], [10, 0, 0]]), 1),
          (np.array([[1, -10, 0], [1, 10, 0]]), 1),
      ]
      for streamline, expected_matched in test_cases:
          mask = np.zeros((2, 1, 1), dtype=np.uint8)
          mask[1, 0, 0] = 1
          matched = list(
              target_line_based([streamline], np.eye(4), mask)
          )
          assert len(matched) == expected_matched
  
imports: import warnings
  import numpy
  import pytest
  from dipy.tracking import metrics
  from dipy.tracking.streamline import transform_streamlines
  from dipy.tracking.utils import connectivity_matrix, density_map, length, ndbincount, reduce_labels, seeds_from_mask, random_seeds_from_mask, target, target_line_based, unique_rows, near_roi, reduce_rois, path_length, _min_at, max_angle_from_curvature, min_radius_curvature_from_angle
  from dipy.tracking._utils import _to_voxel_coordinates
  from dipy.tracking.vox2track import streamline_mapping
  import numpy.testing
  from dipy.testing import assert_true
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 10 for calling a global function with example usage: test_target_line_based_out_of_bounds() without return types