class_docstr: Estimator class based on scikit-learn estimators.
  
  See https://scikit-learn.org/stable/developers/develop.html
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docstr: Fit the model according to the given training data.
  
  Args:
    X: Training array, where the rows are samples, and the column are the
      number of features (assumed to be 1).
    y: Target vector relative to X.
  
  Returns:
    A fitted estimator.
docstr_tok: ["Fit","the","model","according","to","the","given","training","data",".","Args",":","X",":","Training","array",",","where","the","rows","are","samples",",","and","the","column","are","the","number","of","features","(","assumed","to","be","1",")",".","y",":","Target","vector","relative","to","X",".","Returns",":","A","fitted","estimator","."]
code: def fit(
      self, X: jax.Array, y: jax.Array
  ) -> "_AutoBnnEstimator":  # pylint: disable=invalid-name
      """Fit the model according to the given training data.
  
      Args:
        X: Training array, where the rows are samples, and the column are the
          number of features (assumed to be 1).
        y: Target vector relative to X.
  
      Returns:
        A fitted estimator.
      """
      self.likelihood_ = likelihoods.get_likelihood_model(
          self.likelihood_model, self.likelihood_kwargs
      )
      self.net_ = models.make_model(
          model_name=self.model_name,
          likelihood_model=self.likelihood_,
          time_series_xs=X,
          width=self.width,
          periods=self.periods,
      )
      self.fit_seed_, self.predict_quantiles_seed_ = jax.random.split(
          self.seed
      )
      self.params_, self.diagnostics_ = self._fit(
          net=self.net_,
          seed=self.seed,
          x_train=X,
          y_train=y,
          **self.kwargs,
      )
      return self
  
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 4 for calling the _AutoBnnEstimator obj's underlying member method code with example usage: obj.fit(X, y) and returns: self