class_docstr: generic moment tests or conditional moment tests for Quasi-MLE
  
  This is a generic class based on Tauchen 1985
  
  The main method is `chisquare` which returns the result of the
  conditional moment test.
  
  Warning: name of class and of methods will likely be changed
  
  Parameters
  ----------
  score : ndarray, 1-D
      moment condition used in estimation, score of log-likelihood function
  score_deriv : ndarray
      derivative of score function with respect to the parameters that are
      estimated. This is the Hessian in quasi-maximum likelihood
  moments : ndarray, 1-D
      moments that are tested to be zero. They do not need to be derived
      from a likelihood function.
  moments_deriv : ndarray
      derivative of the moment function with respect to the parameters that
      are estimated
  cov_moments : ndarray
      An estimate for the joint (expected) covariance of score and test
      moments. This can be a heteroscedasticity or correlation robust
      covariance estimate, i.e. the inner part of a sandwich covariance.
code: def rank_cov_mom_constraints(self):
      return np.linalg.matrix_rank(self.cov_mom_constraints)
  
imports: import warnings
  import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.regression.linear_model import OLS
label_desc: Use image node_id 4 for calling the CMTTauchen obj's underlying member method code with example usage: obj.rank_cov_mom_constraints() and returns: np