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 check_is_fitted(self) -> bool:
      # The model is fit if any of the real variables (those with trailing
      # underscores) have non-None values. Note that scikit-learn does not
      # initialize these values at all, but we do to help type hinting.
      for v in vars(self):
          if (
              v.endswith("_")
              and not v.startswith("__")
              and (getattr(self, v) is not None)
          ):
              return True
      return False
  
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 2 for calling the _AutoBnnEstimator obj's underlying member method code with example usage: obj.check_is_fitted() and returns: False, True