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 `mean` and `varsqrt` to `xi1`, `xi2`
  
  :param mean: the mean parameter
  :param varsqrt: the varsqrt parameter
  :return: tuple (xi1, xi2), the xi parameters
docstr_tok: ["Transforms","the","parameter","`","mean","`","and","`","varsqrt","`","to","`","xi1","`",",","`","xi2","`",":","param","mean",":","the","mean","parameter",":","param","varsqrt",":","the","varsqrt","parameter",":","return",":","tuple","(","xi1",",","xi2",")",",","the","xi","parameters"]
code: def meanvarsqrt_to_xi(
      mean: tf.Tensor, varsqrt: tf.Tensor
  ) -> Tuple[tf.Tensor, tf.Tensor]:
      """
      Transforms the parameter `mean` and `varsqrt` to `xi1`, `xi2`
  
      :param mean: the mean parameter
      :param varsqrt: the varsqrt parameter
      :return: tuple (xi1, xi2), the xi 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 1 for calling the XiTransform obj's underlying member method code with example usage: obj.meanvarsqrt_to_xi(mean, varsqrt) without return types