code: def test_merge_points_large(dim, radius):
      points = 10 * torch.randn(200, dim)
      merged_points, groups = merge_points(points, radius)
      logger.debug(
          "merged {} -> {}".format(len(points), len(merged_points))
      )
  
      assert merged_points.dim() == 2
      assert merged_points.shape[-1] == dim
      assert len(groups) == len(merged_points)
      assert sum(len(g) for g in groups) == len(points)
      assert set(sum(groups, ())) == set(range(len(points)))
      d2 = (
          (merged_points.unsqueeze(-2) - merged_points.unsqueeze(-3))
          .pow(2)
          .sum(-1)
      )
      assert d2.min() < radius**2
  
file_path: pyro/tests/contrib/tracking/test_hashing.py
imports: import logging
  import pytest
  import torch
  from pyro.contrib.tracking.hashing import LSH, ApproxSet, merge_points
  from tests.common import assert_equal
label_desc: Use image node_id 10 for calling a global function with example usage: test_merge_points_large(dim, radius) without return types