class_docstr: Facade around outputs of `step_kernel`.
  
  Processes results and extracts useful data for analysis and further sampling.
docstr: Generate diagnostics on the outputs.
code: def get_diagnostics(self):
      """Generate diagnostics on the outputs."""
      diagnostics = {}
      acceptance_rate = self.realized_acceptance_rate()
      if acceptance_rate is not None:
          diagnostics["realized_acceptance_rate"] = acceptance_rate
  
      return diagnostics
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/kernel_outputs.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import tracing_reducer
  from tensorflow_probability.python.internal import unnest
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the KernelOutputs obj's underlying member method code with example usage: obj.get_diagnostics() and returns: diagnostics