code: def _skewness(event_shape):
      skewness = torch.zeros(event_shape.numel())
      done = False
      while not done:
          for i in range(event_shape.numel()):
              max_ = 1.0 - skewness.abs().sum(-1)
              if torch.any(max_ < 1e-15):
                  break
              skewness[i] = Uniform(-max_, max_).sample()
          done = not torch.any(max_ < 1e-15)
  
      if event_shape == tuple():
          skewness = skewness.reshape(event_shape)
      else:
          skewness = skewness.view(event_shape)
      return skewness
  
file_path: pyro/tests/distributions/test_sine_skewed.py
imports: from math import pi
  import pytest
  import torch
  import pyro
  from pyro.distributions import Normal, SineSkewed, Uniform, VonMises, constraints
  from pyro.infer import SVI, Trace_ELBO
  from pyro.optim import Adam
  from tests.common import assert_equal
label_desc: Use image node_id 1 for calling a global function with example usage: _skewness(event_shape) and returns: skewness