class_docstr: Nonparametric censored regression.
  
  Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``,
  where y is left-censored.  Left censored variable Y is defined as
  ``Y = min {Y', L}`` where ``L`` is the value at which ``Y`` is censored
  and ``Y'`` is the true value of the variable.
  
  Parameters
  ----------
  endog : list with one element which is array_like
      This is the dependent variable.
  exog : list
      The training data for the independent variable(s)
      Each element in the list is a separate variable
  dep_type : str
      The type of the dependent variable(s)
      c: Continuous
      u: Unordered (Discrete)
      o: Ordered (Discrete)
  reg_type : str
      Type of regression estimator
      lc: Local Constant Estimator
      ll: Local Linear Estimator
  bw : array_like
      Either a user-specified bandwidth or
      the method for bandwidth selection.
      cv_ls: cross-validation least squares
      aic: AIC Hurvich Estimator
  ckertype : str, optional
      The kernel used for the continuous variables.
  okertype : str, optional
      The kernel used for the ordered discrete variables.
  ukertype : str, optional
      The kernel used for the unordered discrete variables.
  censor_val : float
      Value at which the dependent variable is censored
  defaults : EstimatorSettings instance, optional
      The default values for the efficient bandwidth estimation
  
  Attributes
  ----------
  bw : array_like
      The bandwidth parameters
docstr: Returns the marginal effects at the data_predict points.
code: def fit(self, data_predict=None):
      """
      Returns the marginal effects at the data_predict points.
      """
      func = self.est[self.reg_type]
      if data_predict is None:
          data_predict = self.exog
      else:
          data_predict = _adjust_shape(data_predict, self.k_vars)
  
      N_data_predict = np.shape(data_predict)[0]
      mean = np.empty((N_data_predict,))
      mfx = np.empty((N_data_predict, self.k_vars))
      for i in range(N_data_predict):
          mean_mfx = func(
              self.bw,
              self.endog,
              self.exog,
              data_predict=data_predict[i, :],
              W=self.W_in,
          )
          mean[i] = np.squeeze(mean_mfx[0])
          mfx_c = np.squeeze(mean_mfx[1])
          mfx[i, :] = mfx_c
  
      return mean, mfx
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from ._kernel_base import GenericKDE, EstimatorSettings, gpke, LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR, kernel_func
label_desc: Use image node_id 6 for calling the KernelCensoredReg obj's underlying member method code with example usage: obj.fit(data_predict) and returns: mean,  mfx