class_docstr: Estimator class based on scikit-learn estimators.
  
  See https://scikit-learn.org/stable/developers/develop.html
class_docstr_tok: ["Estimator","class","based","on","scikit-learn","estimators",".","See","https",":","\/\/scikit-learn.org\/stable\/developers\/develop.html"]
code: def __init__(
      self,
      model_name: str,
      likelihood_model: str,
      seed: jax.Array,
      width: int = 50,
      periods: Sequence[ArrayLike] = (12.0,),
      likelihood_kwargs: Optional[Mapping[str, Any]] = None,
  ):
      self.model_name = model_name
      self.likelihood_model = likelihood_model
      self.width = width
      self.periods = periods
      self.seed = seed
      if likelihood_kwargs is not None:
          self.likelihood_kwargs = likelihood_kwargs
      else:
          self.likelihood_kwargs = {}
  
      self.net_: Optional[bnn.BNN] = None
      self.params_: Optional[PyTree] = None
      self.diagnostics_: Optional[dict[str, jax.Array]] = None
      self.fit_seed_: Optional[jax.Array] = None
      self.predict_quantiles_seed_: Optional[jax.Array] = None
      self.likelihood_: Optional[likelihoods.LikelihoodModel] = None
      self.kwargs = {}
  
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 _AutoBnnEstimator object  with example: obj = _AutoBnnEstimator(model_name, likelihood_model, seed, width, periods, likelihood_kwargs)