class_docstr: Applies a recommended set of reparametrizers. These currently include:
  :class:`MinimalReparam`,
  :class:`~pyro.infer.reparam.transform.TransformReparam`, a fully-learnable
  :class:`~pyro.infer.reparam.loc_scale.LocScaleReparam`, and
  :class:`~pyro.infer.reparam.softmax.GumbelSoftmaxReparam`.
  
  Example::
  
      @AutoReparam()
      def model(...):
          ...
  
  which is equivalent to::
  
      @poutine.reparam(config=AutoReparam())
      def model(...):
          ...
  
  .. warning:: This strategy may change behavior across Pyro releases.
      To inspect or save a given behavior, extract the ``.config`` dict after
      running the model at least once.
  
  :param centered: Optional centering parameter for
      :class:`~pyro.infer.reparam.loc_scale.LocScaleReparam` reparametrizers.
      If None (default), centering will be learned. If a float in
      ``[0.0,1.0]``, then a fixed centering. To completely decenter (e.g. in
      MCMC), set to 0.0.
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code: def __init__(self, *, centered: Optional[float] = None):
      assert centered is None or isinstance(centered, float)
      super().__init__()
      self.centered = centered
  
imports: from abc import ABC, abstractmethod
  from typing import Callable, Dict, Optional, Union
  import torch
  from torch.distributions import constraints
  import pyro.distributions
  import pyro.poutine
  from .loc_scale import LocScaleReparam
  from .projected_normal import ProjectedNormalReparam
  from .reparam import Reparam
  from .softmax import GumbelSoftmaxReparam
  from .stable import LatentStableReparam, StableReparam, SymmetricStableReparam
  from .transform import TransformReparam
label_desc: Use image node_id 1 to create a new AutoReparam object from inherited base classes: Strategy with example: obj = AutoReparam()