code: def test_log_survival_function():
      rng = numpy.random.default_rng(0xFEEEFEEE)
  
      batch_size = 32
  
      loc = rng.standard_normal(batch_size, dtype=numpy.float64)
  
      scale = rng.random(batch_size, dtype=numpy.float64) + 1.0
  
      x = numpy.linspace(-8.0, 8.0, batch_size, dtype=numpy.float64)
  
      torch.testing.assert_close(
          log_survival_function(
              torch.tensor(x, dtype=torch.float),
              Normal(
                  torch.tensor(loc, dtype=torch.float),
                  torch.tensor(scale, dtype=torch.float),
              ),
          ),
          torch.tensor(
              scipy.stats.norm(loc, scale).logsf(x), dtype=torch.float
          ),
      )
  
imports: import numpy
  import scipy.stats
  import torch
  import torch.testing
  from torch.distributions import Normal
  from neuralcompression.functional import log_survival_function
label_desc: Use image node_id 1 for calling a global function with example usage: test_log_survival_function() without return types