class_docstr: Represents probability space of stochastic processes
  and their random variables. Contains mechanics to do
  computations for queries of stochastic processes.
  
  Explanation
  ===========
  
  Initialized by symbol, the specific process and
  distribution(optional) if the random indexed symbols
  of the process follows any specific distribution, like,
  in Bernoulli Process, each random indexed symbol follows
  Bernoulli distribution. For processes with memory, this
  parameter should not be passed.
docstr: Transfers the task of handling queries to the specific stochastic
  process because every process has their own logic of handling such
  queries.
code: def compute_expectation(
      self, expr, condition=None, evaluate=True, **kwargs
  ):
      """
      Transfers the task of handling queries to the specific stochastic
      process because every process has their own logic of handling such
      queries.
      """
      return self.process.expectation(
          expr, condition, evaluate, **kwargs
      )
  
file_path: sympy/sympy/stats/stochastic_process.py
imports: from sympy.core.basic import Basic
  from sympy.stats.joint_rv import ProductPSpace
  from sympy.stats.rv import ProductDomain, _symbol_converter, Distribution
label_desc: Use image node_id 7 for calling the StochasticPSpace obj's underlying member method code with example usage: obj.compute_expectation(expr, condition, evaluate) and returns: self