class_docstr: Applies `Reducer`s to stream over MCMC samples.
  
  `WithReductions` augments an inner MCMC kernel with
  side-computations that can read the stream of samples as they
  are generated.  This is relevant for streaming uses of MCMC,
  where materializing the entire Markov chain history is undesirable,
  e.g. due to memory limits.
  
  One `WithReductions` instance can attach an arbitrary collection
  of side-computations, each of which must be packaged as a
  `Reducer`.  `WithReductions` operates by generating a
  sample with its `inner_kernel`'s `one_step`, then invoking each
  `Reducer`'s `one_step` method on that sample. The updated reducer
  states are stored in the `reduction_results` field of
  `WithReductions`' kernel results.
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code: def name(self):
      return self._parameters["name"]
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/with_reductions.py
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc.internal import util
  from tensorflow.python.util import nest
label_desc: Use image node_id 9 for calling the WithReductions obj's underlying member method code with example usage: obj.name() and returns: self