class_docstr: Base class that all volatility updaters must inherit from.
  
  Notes
  -----
  See the implementation available for information on modifying ``__init__``
  to capture model-specific parameters and how ``initialize_update`` is
  used to precompute values that change in each likelihood but not
  each iteration of the recursion.
  
  When writing a volatility updater, it is recommended to follow the
  examples in recursions.pyx which use Cython to produce a C-callable
  update function that can then be used to improve performance. The
  subclasses of this abstract metaclass are all pure Python and
  model estimation performance is poor since loops are written
  in Python.
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docstr: Update the current variance at location t
  
  Parameters
  ----------
  t : int
      The index of the value of sigma2 to update. Assumes but does not check
      that update has been called recursively for 0,1,...,t-1.
  parameters : ndarray
      Model parameters
  resids : ndarray
      Residuals to use in the recursion
  sigma2 : ndarray
      Conditional variances with same shape as resids
  var_bounds : ndarray
      nobs by 2-element array of upper and lower bounds for conditional
      variances for each time period
  
  Notes
  -----
  The update to sigma2 occurs inplace.
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code: def update(
      self,
      t: int,
      parameters: Float64Array,
      resids: Float64Array,
      sigma2: Float64Array,
      var_bounds: Float64Array,
  ) -> None:
      """
      Update the current variance at location t
  
      Parameters
      ----------
      t : int
          The index of the value of sigma2 to update. Assumes but does not check
          that update has been called recursively for 0,1,...,t-1.
      parameters : ndarray
          Model parameters
      resids : ndarray
          Residuals to use in the recursion
      sigma2 : ndarray
          Conditional variances with same shape as resids
      var_bounds : ndarray
          nobs by 2-element array of upper and lower bounds for conditional
          variances for each time period
  
      Notes
      -----
      The update to sigma2 occurs inplace.
      """
      pass
  
imports: from __future__ import annotations
  from arch.compat.numba import jit
  from abc import ABCMeta, abstractmethod
  from typing import cast
  import numpy
  from scipy.special import gammaln
  from arch.typing import Float64Array, Int32Array
  from arch.utility.array import AbstractDocStringInheritor
label_desc: Use image node_id 3 for calling the VolatilityUpdater obj's underlying member method code with example usage: obj.update(t, parameters, resids, sigma2, var_bounds) without return types