class_docstr: Helper class for construction loss and updates for variational inference
  
  Parameters
  ----------
  op: :class:`KSD`
      OPVI Functional operator
  tf: :class:`TestFunction`
      OPVI TestFunction
class_docstr_tok: ["Helper","class","for","construction","loss","and","updates","for","variational","inference","Parameters","--","--","--","--","--","op",":",":","class",":","`","KSD","`","OPVI","Functional","operator","tf",":",":","class",":","`","TestFunction","`","OPVI","TestFunction"]
code: def __init__(self, op: KSD, tf: opvi.TestFunction):
      if not isinstance(op, KSD):
          raise opvi.ParametrizationError("Op should be KSD")
      super().__init__(op, tf)
  
file_path: pymc/pymc/variational/operators.py
imports: from __future__ import annotations
  import pytensor
  from pytensor.graph.basic import Variable
  import pymc
  from pymc.variational import opvi
  from pymc.variational.opvi import NotImplementedInference, ObjectiveFunction, Operator, _known_scan_ignored_inputs
  from pymc.variational.stein import Stein
label_desc: Use image node_id 1 to create a new KSDObjective object from inherited base classes: ObjectiveFunction with example: obj = KSDObjective(op, tf)