code: def __init__(self, T, a, sig, *args, **kwargs):
      self.a = a
      self.sig = sig
      self.T = T
      self.shocks = Normal(tf.zeros(T), scale=sig)
      self.z = tf.scan(lambda acc, x: self.a * acc + x, self.shocks)
  
      if "dtype" not in kwargs:
          kwargs["dtype"] = tf.float32
      if "allow_nan_stats" not in kwargs:
          kwargs["allow_nan_stats"] = False
      if "reparameterization_type" not in kwargs:
          kwargs["reparameterization_type"] = FULLY_REPARAMETERIZED
      if "validate_args" not in kwargs:
          kwargs["validate_args"] = False
      if "name" not in kwargs:
          kwargs["name"] = "AutoRegressive"
  
      super(AutoRegressive, self).__init__(*args, **kwargs)
  
      self._args = (T, a, sig)
  
file_path: edward/tests/inferences/ar_process_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import tensorflow
  from edward.models import Normal, PointMass
  from scipy.optimize import minimize
  from edward.models import RandomVariable
  from tensorflow.contrib.distributions import Distribution
  from tensorflow.contrib.distributions import FULLY_REPARAMETERIZED
label_desc: Use image node_id 1 to create a new AutoRegressive object from inherited base classes: RandomVariable, Distribution with example: obj = AutoRegressive(T, a, sig)