class_docstr: Multivariate Linear Model estimated by least squares.
  
  Parameters
  ----------
  endog : array_like
      Dependent variables. A nobs x k_endog array where nobs is
      the number of observations and k_endog is the number of dependent
      variables
  exog : array_like
      Independent variables. A nobs x k_exog array where nobs is the
      number of observations and k_exog is the number of independent
      variables. An intercept is not included by default and should be added
      by the user (models specified using a formula include an intercept by
      default)
class_docstr_tok: ["Multivariate","Linear","Model","estimated","by","least","squares",".","Parameters","--","--","--","--","--","endog",":","array_like","Dependent","variables",".","A","nobs","x","k_endog","array","where","nobs","is","the","number","of","observations","and","k_endog","is","the","number","of","dependent","variables","exog",":","array_like","Independent","variables",".","A","nobs","x","k_exog","array","where","nobs","is","the","number","of","observations","and","k_exog","is","the","number","of","independent","variables",".","An","intercept","is","not","included","by","default","and","should","be","added","by","the","user","(","models","specified","using","a","formula","include","an","intercept","by","default",")"]
code: def fit(self, method="svd", use_t=True):
      _fittedmod = _multivariate_ols_fit(
          self.endog, self.exog, method=method
      )
  
      params, df_resid, inv_cov, sscpr = _fittedmod
      normalized_cov_params = np.kron(sscpr, inv_cov) / df_resid
      self.df_resid = df_resid
      res = MultivariateLSResults(
          self,
          params,
          normalized_cov_params=normalized_cov_params,
          scale=1,
          use_t=use_t,
          # extra kwargs are currently ignored
      )
  
      res.df_resid = self.df_resid = df_resid
      res._fittedmod = _fittedmod
      res.cov_resid = sscpr / df_resid
      return MultivariateLSResultsWrapper(res)
      return res
  
imports: import numpy
  from numpy.linalg import eigvals, inv, solve, matrix_rank, pinv, svd
  from scipy import stats
  import pandas
  from patsy import DesignInfo
  from statsmodels.compat.pandas import Substitution
  from statsmodels.base.model import Model, LikelihoodModelResults
  import statsmodels.base.wrapper
  from statsmodels.regression.linear_model import RegressionResultsWrapper
  from statsmodels.iolib import summary2
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 1 for calling the MultivariateLS obj's underlying member method code with example usage: obj.fit(method, use_t) and returns: MultivariateLSResultsWrapper, res