class_docstr: Significance test for continuous variables in a nonparametric regression.
  
  The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis
  is ``dE(Y|X)/dX_not_i != 0``.
  
  Parameters
  ----------
  model : KernelReg instance
      This is the nonparametric regression model whose elements
      are tested for significance.
  test_vars : tuple, list of integers, array_like
      index of position of the continuous variables to be tested
      for significance. E.g. (1,3,5) jointly tests variables at
      position 1,3 and 5 for significance.
  nboot : int
      Number of bootstrap samples used to determine the distribution
      of the test statistic in a finite sample. Default is 400
  nested_res : int
      Number of nested resamples used to calculate lambda.
      Must enable the pivot option
  pivot : bool
      Pivot the test statistic by dividing by its standard error
      Significantly increases computational time. But pivot statistics
      have more desirable properties
      (See references)
  
  Attributes
  ----------
  sig : str
      The significance level of the variable(s) tested
      "Not Significant": Not significant at the 90% confidence level
                          Fails to reject the null
      "*": Significant at the 90% confidence level
      "**": Significant at the 95% confidence level
      "***": Significant at the 99% confidence level
  
  Notes
  -----
  This class allows testing of joint hypothesis as long as all variables
  are continuous.
  
  References
  ----------
  Racine, J.: "Consistent Significance Testing for Nonparametric Regression"
  Journal of Business & Economics Statistics.
  
  Chapter 12 in [1].
code: def run(self):
      self.test_stat = self._compute_test_stat(self.endog, self.exog)
      self.sig = self._compute_sig()
  
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 2 for calling the TestRegCoefC obj's underlying member method code with example usage: obj.run() without return types