class_docstr: Base class of SEM estimators. All the estimators inherit this class.
class_docstr_tok: ["Base","class","of","SEM","estimators",".","All","the","estimators","inherit","this","class","."]
docstr: Computes the implied covariance matrix from the given parameters.
docstr_tok: ["Computes","the","implied","covariance","matrix","from","the","given","parameters","."]
code: def _get_implied_cov(self, B, zeta):
      """
      Computes the implied covariance matrix from the given parameters.
      """
      B_masked = torch.mul(B, self.B_mask) + self.B_fixed_mask
      B_inv = pinverse(self.B_eye - B_masked)
      zeta_masked = (
          torch.mul(zeta, self.zeta_mask) + self.zeta_fixed_mask
      )
  
      return (
          self.wedge_y
          @ B_inv
          @ zeta_masked
          @ B_inv.t()
          @ self.wedge_y.t()
      )
  
file_path: pgmpy/pgmpy/estimators/SEMEstimator.py
imports: import numpy
  import pandas
  import statsmodels.api
  import torch
  from pgmpy import config
  from pgmpy.models import SEM, SEMAlg, SEMGraph
  from pgmpy.utils import optimize, pinverse
label_desc: Use image node_id 2 for calling the SEMEstimator obj's underlying member method code with example usage: obj._get_implied_cov(B, zeta) and returns: unknown