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 predict(self, params, exog=None):
      if exog is None:
          exog = self.exog
      else:
          exog = np.asarray(exog)
      return exog @ params
  
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 2 for calling the MultivariateLS obj's underlying member method code with example usage: obj.predict(params, exog) and returns: unknown