class_docstr: Transforms the diagonal elements of the LKJCholeskyCov distribution to be on the
  log scale
class_docstr_tok: ["Transforms","the","diagonal","elements","of","the","LKJCholeskyCov","distribution","to","be","on","the","log","scale"]
docstr: Parameters
  ----------
  n: int
      Number of diagonal entries in the LKJCholeskyCov distribution
docstr_tok: ["Parameters","--","--","--","--","--","n",":","int","Number","of","diagonal","entries","in","the","LKJCholeskyCov","distribution"]
code: def __init__(self, n):
      """
  
      Parameters
      ----------
      n: int
          Number of diagonal entries in the LKJCholeskyCov distribution
      """
      self.diag_idxs = pt.arange(1, n + 1).cumsum() - 1
  
imports: import warnings
  from functools import singledispatch
  import numpy
  import pytensor.tensor
  from numpy.core.numeric import normalize_axis_tuple
  from pytensor.graph import Op
  from pytensor.tensor import TensorVariable
  import pymc
  from pymc.logprob.transforms import ChainedTransform, CircularTransform, IntervalTransform, LogOddsTransform, LogTransform, SimplexTransform, Transform
label_desc: Use image node_id 1 to create a new CholeskyCovPacked object from inherited base classes: Transform with example: obj = CholeskyCovPacked(n)