class_docstr: Asymptotic covariance by Deltamethod
  
  The function is designed for 2d array, with rows equal to
  the number of equations or constraints and columns equal to the number
  of parameters. 1d params work by chance ?
  
  fun: R^{m*k) -> R^{m}  where m is number of equations and k is
  the number of parameters.
  
  equations follow Greene
  
  This class does not use any caching. The intended usage is as a helper
  function. Extra methods have been added for convenience but might move
  to calling functions.
  
  The naming in this class uses params for the original random variable, and
  cov_params for it's covariance matrix. However, this class is independent
  of the use cases in support of the models.
  
  Parameters
  ----------
  func : callable, f(params)
      Nonlinear function of the estimation parameters. The return of
      the function can be vector valued, i.e. a 1-D array.
  params : ndarray
      Parameters at which function `func` is evaluated.
  cov_params : ndarray
      Covariance matrix of the parameters `params`.
  deriv : function or None
      First derivative or Jacobian of func. If deriv is None, then a
      numerical derivative will be used. If func returns a 1-D array,
      then the `deriv` should have rows corresponding to the elements
      of the return of func.
  func_args : None
      Not yet implemented.
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docstr: standard error for each equation (row) treated separately
  
          
docstr_tok: ["standard","error","for","each","equation","(","row",")","treated","separately"]
code: def var(self):
      """standard error for each equation (row) treated separately"""
      g = self.grad()
      var = (np.dot(g, self.cov_params) * g).sum(-1)
  
      if var.ndim == 2:
          var = var.T
      return var
  
file_path: statsmodels/statsmodels/stats/_delta_method.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 6 for calling the NonlinearDeltaCov obj's underlying member method code with example usage: obj.var() and returns: var