class_docstr: `Reducer` that computes a running expectation.
  
  `ExpectationsReducer` calculates expectations over some arbitrary structure
  of `transform_fn`s. A `transform_fn` is a function that accepts a Markov
  chain sample and kernel results, and outputs the relevant value for
  expectation calculation. In other words, if we denote a `transform_fn`
  as f(x,y), `ExpectationsReducer` approximates E[f(x,y)] for all provided
  functions. The finalized expectation will also identically mimic the
  structure of `transform_fn`.
  
  As with all reducers, ExpectationsReducer does not hold state information;
  rather, it stores supplied metadata. Intermediate calculations are held in
  a state object, which is returned via `initialize` and `one_step` method
  calls.
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code: def parameters(self):
      return self._parameters
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/expectations_reducer.py
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import reducer
  from tensorflow_probability.python.experimental.stats import sample_stats
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.mcmc.internal import util
  from tensorflow.python.util import nest
label_desc: Use image node_id 7 for calling the ExpectationsReducer obj's underlying member method code with example usage: obj.parameters() and returns: self