class_docstr: Object to store runtime and memory profiling information for all of
  Theano's operations: compilation, optimization, execution.
  
  Parameters
  ----------
  atexit_print : bool
      True means that this object will be printed to stderr (using .summary())
      at the end of the program.
  **kwargs : misc initializers
      These should (but need not) match the names of the class vars declared
      in this class.
class_docstr_tok: ["Object","to","store","runtime","and","memory","profiling","information","for","all","of","Theano","'s","operations",":","compilation",",","optimization",",","execution",".","Parameters","--","--","--","--","--","atexit_print",":","bool","True","means","that","this","object","will","be","printed","to","stderr","(","using",".summary","(",")",")","at","the","end","of","the","program",".","*","*","kwargs",":","misc","initializers","These","should","(","but","need","not",")","match","the","names","of","the","class","vars","declared","in","this","class","."]
docstr: dict op -> total number of thunk calls
docstr_tok: ["dict","op","-",">","total","number","of","thunk","calls"]
code: def op_callcount(self):
      """
      dict op -> total number of thunk calls
  
      """
      # timing is stored by node, we compute timing by Op on demand
      rval = {}
      for node, count in iteritems(self.apply_callcount):
          rval.setdefault(node.op, 0)
          rval[node.op] += count
      return rval
  
imports: from __future__ import absolute_import, print_function, division
  import atexit
  import copy
  import logging
  import operator
  import os
  import sys
  import time
  from collections import defaultdict
  from six import iteritems
  import warnings
  import numpy
  import theano
  from theano.gof import graph
label_desc: Use image node_id 10 for calling the ProfileStats obj's underlying member method code with example usage: obj.op_callcount() and returns: rval