class_docstr: Semiparametric partially linear model, ``Y = Xb + g(Z) + e``.
  
  Parameters
  ----------
  endog : array_like
      The dependent variable
  exog : array_like
      The linear component in the regression
  exog_nonparametric : array_like
      The nonparametric component in the regression
  var_type : str
      The type of the variables in the nonparametric component;
  
          - c: continuous
          - o: ordered
          - u: unordered
  
  k_linear : int
      The number of variables that comprise the linear component.
  
  Attributes
  ----------
  bw : array_like
      Bandwidths for the nonparametric component exog_nonparametric
  b : array_like
      Coefficients in the linear component
  nobs : int
      The number of observations.
  k_linear : int
      The number of variables that comprise the linear component.
  
  Methods
  -------
  fit
      Returns the fitted mean and marginal effects dy/dz
  
  Notes
  -----
  This model uses only the local constant regression estimator
  
  References
  ----------
  See chapter on Semiparametric Models in [1]
code: def __init__(
      self, endog, exog, exog_nonparametric, var_type, k_linear
  ):
      self.endog = _adjust_shape(endog, 1)
      self.exog = _adjust_shape(exog, k_linear)
      self.K = len(var_type)
      self.exog_nonparametric = _adjust_shape(
          exog_nonparametric, self.K
      )
      self.k_linear = k_linear
      self.nobs = np.shape(self.exog)[0]
      self.var_type = var_type
      self.data_type = self.var_type
      self.ckertype = "gaussian"
      self.okertype = "wangryzin"
      self.ukertype = "aitchisonaitken"
      self.func = self._est_loc_linear
  
      self.b, self.bw = self._est_b_bw()
  
imports: import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from statsmodels.nonparametric.api import KDEMultivariate, KernelReg
  from statsmodels.nonparametric._kernel_base import gpke, LeaveOneOut, _get_type_pos, _adjust_shape
label_desc: Use image node_id 1 to create a new SemiLinear object from inherited base classes: KernelReg with example: obj = SemiLinear(endog, exog, exog_nonparametric, var_type, k_linear)