code: def fit(
      self,
      X,
      y,
      y_error=1,
      x_error=None,
      *,
      sample_kwargs={"draws": 1000, "target_accept": 0.9},
  ):
      if not PYMC_LT_39:
          sample_kwargs["return_inferencedata"] = False
  
      kwds = {}
      if self.kwds is not None:
          kwds.update(self.kwds)
      kwds["fit_intercept"] = False
      model = self._choose_regressor()
      self.clf_ = model(**kwds)
  
      self.fit_intercept = False
  
      if x_error is not None:
          x_error = np.atleast_2d(x_error)
      with pm.Model():
          # slope and intercept of eta-ksi relation
          slope = pm.Flat("slope", shape=(X.shape[0],))
          inter = pm.Flat("inter")
  
          # intrinsic scatter of eta-ksi relation
          int_std = pm.HalfFlat("int_std")
          # standard deviation of Gaussian that ksi are drawn from (assumed mean zero)
          tau = pm.HalfFlat("tau", shape=(X.shape[0],))
          # intrinsic ksi
          mu = pm.Normal("mu", mu=0, sigma=tau, shape=(X.shape[0],))
  
          # Some wizzarding with the dimensions all around.
          ksi = pm.Normal("ksi", mu=mu, tau=tau, shape=X.T.shape)
  
          # intrinsic eta-ksi linear relation + intrinsic scatter
          eta = pm.Normal(
              "eta",
              mu=(tt.dot(slope.T, ksi.T) + inter),
              sigma=int_std,
              shape=y.shape,
          )
  
          # observed xi, yi
          x = pm.Normal(
              "xi", mu=ksi.T, sigma=x_error, observed=X, shape=X.shape
          )  # noqa: F841
          y = pm.Normal(
              "yi", mu=eta, sigma=y_error, observed=y, shape=y.shape
          )
  
          self.trace = pm.sample(**sample_kwargs)
  
          # TODO: make it optional to choose a way to define best
  
          HND, edges = np.histogramdd(
              np.hstack(
                  (self.trace["slope"], self.trace["inter"][:, None])
              ),
              bins=50,
          )
  
          w = np.where(HND == HND.max())
  
          # choose the maximum posterior slope and intercept
          slope_best = [
              edges[i][w[i][0]] for i in range(len(edges) - 1)
          ]
          intercept_best = edges[-1][w[-1][0]]
          self.clf_.coef_ = np.array([intercept_best, *slope_best])
  
      return self
  
imports: import numpy
  import warnings
  from astroML.linear_model import LinearRegression
label_desc: Use image node_id 2 for calling the LinearRegressionwithErrors obj's underlying member method code with example usage: obj.fit(X, y, y_error, x_error) and returns: self