class_docstr: Implementation of an MCMC estimator for the BNN.
class_docstr_tok: ["Implementation","of","an","MCMC","estimator","for","the","BNN","."]
code: def __init__(
      self,
      model_name: str,
      likelihood_model: str,
      seed: jax.Array,
      width: int = 50,
      periods: Sequence[ArrayLike] = (12.0,),
      num_chains: int = 128,
      num_draws: int = 8,
      likelihood_kwargs: Optional[Mapping[str, Any]] = None,
      **unused_kwargs,
  ):
      super().__init__(
          model_name=model_name,
          likelihood_model=likelihood_model,
          seed=seed,
          width=width,
          periods=periods,
          likelihood_kwargs=likelihood_kwargs,
      )
      self.num_chain = num_chains
      self.num_draws = num_draws
      self.kwargs = {"num_chains": num_chains, "num_draws": num_draws}
  
      self.diagnostics_ = None
      self._fit = training_util.fit_bnn_mcmc
  
imports: from typing import Any, Mapping, Optional, Sequence
  import jax
  import jax.numpy
  from jaxtyping import ArrayLike, PyTree
  from tensorflow_probability.python.experimental.autobnn import bnn
  from tensorflow_probability.python.experimental.autobnn import likelihoods
  from tensorflow_probability.python.experimental.autobnn import models
  from tensorflow_probability.python.experimental.autobnn import training_util
label_desc: Use image node_id 1 to create a new AutoBnnMCMCEstimator object from inherited base classes: _AutoBnnEstimator with example: obj = AutoBnnMCMCEstimator(model_name, likelihood_model, seed, width, periods, num_chains, num_draws, likelihood_kwargs)