class_docstr: XiTransform is the base class that implements three transformations necessary
  for the natural gradient calculation wrt any parameterization.
class_docstr_tok: ["XiTransform","is","the","base","class","that","implements","three","transformations","necessary","for","the","natural","gradient","calculation","wrt","any","parameterization","."]
docstr: Transforms the parameter `xi1`, `xi2` to `mean`, `varsqrt`
  
  :param xi1: the ξ₁ parameter
  :param xi2: the ξ₂ parameter
  :return: tuple (mean, varsqrt), the meanvarsqrt parameters
docstr_tok: ["Transforms","the","parameter","`","xi1","`",",","`","xi2","`","to","`","mean","`",",","`","varsqrt","`",":","param","xi1",":","the","\u03be\u2081","parameter",":","param","xi2",":","the","\u03be\u2082","parameter",":","return",":","tuple","(","mean",",","varsqrt",")",",","the","meanvarsqrt","parameters"]
code: def xi_to_meanvarsqrt(
      xi1: tf.Tensor, xi2: tf.Tensor
  ) -> Tuple[tf.Tensor, tf.Tensor]:
      """
      Transforms the parameter `xi1`, `xi2` to `mean`, `varsqrt`
  
      :param xi1: the ξ₁ parameter
      :param xi2: the ξ₂ parameter
      :return: tuple (mean, varsqrt), the meanvarsqrt parameters
      """
  
imports: import abc
  import functools
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
  import tensorflow
  from check_shapes import check_shapes
  from ..base import AnyNDArray, Parameter, _to_constrained
label_desc: Use image node_id 2 for calling the XiTransform obj's underlying member method code with example usage: obj.xi_to_meanvarsqrt(xi1, xi2) without return types