class_docstr: A data structure to cache the sequence of functional calls.
  
  Each autoobject contains a callcache object. The call sequences will
  be gradually passed down to the last function call.
  Internally, it containes a callqueue which collects the function calls in    order and a tensors table which is used to collect the resulting    tensors from function execution.
class_docstr_tok: ["A","data","structure","to","cache","the","sequence","of","functional","calls",".","Each","autoobject","contains","a","callcache","object",".","The","call","sequences","will","be","gradually","passed","down","to","the","last","function","call",".","Internally",",","it","containes","a","callqueue","which","collects","the","function","calls","in","order","and","a","tensors","table","which","is","used","to","collect","the","resulting","tensors","from","function","execution","."]
docstr: Dump the call cache for debugging purpose.
docstr_tok: ["Dump","the","call","cache","for","debugging","purpose","."]
code: def plot(self, save_path=None):
      """Dump the call cache for debugging purpose."""
      print("dumping call cache...............start")
      print("===============dumpping call queue============")
      for call in self.callqueue_:
          print(call)
      print("===============dumpping tensors============")
      print(self.tensors_)
      print("dumping call cache...............end")
  
file_path: BigDL/python/nano/src/bigdl/nano/automl/hpo/callgraph.py
imports: from .space import AutoObject
  from enum import Enum
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 11 for calling the CallCache obj's underlying member method code with example usage: obj.plot(save_path) without return types