class_docstr: Tests the Autoregressive distribution.
class_docstr_tok: ["Tests","the","Autoregressive","distribution","."]
code: def _random_scale_tril(self, event_size):
      n = np.int32(event_size * (event_size + 1) // 2)
      p = 2.0 * self._rng.random_sample(n).astype(np.float32) - 1.0
      return linalg.fill_triangular(0.25 * p)
  
imports: import warnings
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import masked_autoregressive
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.distributions import autoregressive
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import linalg
label_desc: Use image node_id 2 for calling the AutoregressiveTest obj's underlying member method code with example usage: obj._random_scale_tril(event_size) and returns: linalg