class_docstr: Fit VAR and then estimate structural components of A and B, defined:
  
  .. math:: Ay_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + B\var(\epsilon_t)
  
  Parameters
  ----------
  endog : array_like
      1-d endogenous response variable. The independent variable.
  dates : array_like
      must match number of rows of endog
  svar_type : str
      "A" - estimate structural parameters of A matrix, B assumed = I
      "B" - estimate structural parameters of B matrix, A assumed = I
      "AB" - estimate structural parameters indicated in both A and B matrix
  A : array_like
      neqs x neqs with unknown parameters marked with 'E' for estimate
  B : array_like
      neqs x neqs with unknown parameters marked with 'E' for estimate
  
  References
  ----------
  Hamilton (1994) Time Series Analysis
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code: def check_rank(self, J):
      rank = np.linalg.matrix_rank(J)
      if rank < np.size(J, axis=1):
          raise ValueError(
              "Rank condition not met: " "solution may not be unique."
          )
  
imports: import numpy
  import numpy.linalg
  from numpy.linalg import slogdet
  from statsmodels.tools.decorators import deprecated_alias
  from statsmodels.tools.numdiff import approx_fprime, approx_hess
  import statsmodels.tsa.base.tsa_model
  from statsmodels.tsa.vector_ar.irf import IRAnalysis
  import statsmodels.tsa.vector_ar.util
  from statsmodels.tsa.vector_ar.var_model import VARProcess, VARResults
label_desc: Use image node_id 11 for calling the SVAR obj's underlying member method code with example usage: obj.check_rank(J) without return types