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 summary(self) -> str:
      if self.net_ is None:
          return ""
      if self.params_ is None:
          return self.net_.summarize(None)
      params_per_particle = training_util.debatchify_params(
          self.params_
      )
      summaries = [self.net_.summarize(p) for p in params_per_particle]
      return "\n".join(summaries)
  
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 7 for calling the _AutoBnnEstimator obj's underlying member method code with example usage: obj.summary() and returns: str, str, self