class_docstr: Class represents a known SVAR(p) process
  
  Parameters
  ----------
  coefs : ndarray (p x k x k)
  intercept : ndarray (length k)
  sigma_u : ndarray (k x k)
  names : sequence (length k)
  A : neqs x neqs np.ndarray with unknown parameters marked with 'E'
  A_mask : neqs x neqs mask array with known parameters masked
  B : neqs x neqs np.ndarry with unknown parameters marked with 'E'
  B_mask : neqs x neqs mask array with known parameters masked
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code: def __init__(
      self, coefs, intercept, sigma_u, A_solve, B_solve, names=None
  ):
      self.k_ar = len(coefs)
      self.neqs = coefs.shape[1]
      self.coefs = coefs
      self.intercept = intercept
      self.sigma_u = sigma_u
      self.A_solve = A_solve
      self.B_solve = B_solve
      self.names = names
  
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 1 to create a new SVARProcess object from inherited base classes: VARProcess with example: obj = SVARProcess(coefs, intercept, sigma_u, A_solve, B_solve, names)