class_docstr: Constrains any random samples to sum to zero along the user-provided ``zerosum_axes``.
  
  Parameters
  ----------
  zerosum_axes : list of ints
      Must be a list of integers (positive or negative).
class_docstr_tok: ["Constrains","any","random","samples","to","sum","to","zero","along","the","user-provided","``","zerosum_axes","``",".","Parameters","--","--","--","--","--","zerosum_axes",":","list","of","ints","Must","be","a","list","of","integers","(","positive","or","negative",")","."]
code: def log_jac_det(self, value, *rv_inputs):
      return pt.constant(0.0)
  
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 6 for calling the ZeroSumTransform obj's underlying member method code with example usage: obj.log_jac_det(value) and returns: pt