class_docstr: Adapts tunable parameters, namely step size and mass matrix, during the
  warmup phase. This class provides lookup properties to read the latest
  values of ``step_size`` and ``inverse_mass_matrix``. These values are
  periodically updated when adaptation is engaged.
class_docstr_tok: ["Adapts","tunable","parameters",",","namely","step","size","and","mass","matrix",",","during","the","warmup","phase",".","This","class","provides","lookup","properties","to","read","the","latest","values","of","``","step_size","``","and","``","inverse_mass_matrix","``",".","These","values","are","periodically","updated","when","adaptation","is","engaged","."]
docstr: Finds a reasonable step size and resets step size adaptation scheme.
docstr_tok: ["Finds","a","reasonable","step","size","and","resets","step","size","adaptation","scheme","."]
code: def reset_step_size_adaptation(self, z):
      r"""
      Finds a reasonable step size and resets step size adaptation scheme.
      """
      if self._find_reasonable_step_size is not None:
          with pyro.validation_enabled(False):
              self.step_size = self._find_reasonable_step_size(z)
      self._step_size_adapt_scheme.prox_center = math.log(
          10 * self.step_size
      )
      self._step_size_adapt_scheme.reset()
  
imports: import math
  from collections import namedtuple
  import torch
  import pyro
  from pyro.ops.arrowhead import SymmArrowhead, sqrt, triu_gram, triu_inverse, triu_matvecmul
  from pyro.ops.dual_averaging import DualAveraging
  from pyro.ops.welford import WelfordArrowheadCovariance, WelfordCovariance
label_desc: Use image node_id 3 for calling the WarmupAdapter obj's underlying member method code with example usage: obj.reset_step_size_adaptation(z) without return types