(self, covmat_sqrt, b)
return self.cvxpylayer(covmat_sqrt, b)[0]
Perform forward pass.

        Parameters
        ----------
        covmat : torch.Tensor
            Of shape (n_samples, n_assets, n_assets) representing the covariance matrix.

        b : torch.Tensor
            Of shape (n_samples, n_assets) representing the budget,
            risk contribution from each component (asset) is equal to the budget, refer [3]

        Returns
        -------
        weights : torch.Tensor
            Of shape (n_samples, n_assets) representing the optimal weights as determined by the convex optimizer.
Perform forward pass.
Perform forward pass .
def forward(self, covmat_sqrt, b):
        """Perform forward pass.

        Parameters
        ----------
        covmat : torch.Tensor
            Of shape (n_samples, n_assets, n_assets) representing the covariance matrix.

        b : torch.Tensor
            Of shape (n_samples, n_assets) representing the budget,
            risk contribution from each component (asset) is equal to the budget, refer [3]

        Returns
        -------
        weights : torch.Tensor
            Of shape (n_samples, n_assets) representing the optimal weights as determined by the convex optimizer.

        """
        return self.cvxpylayer(covmat_sqrt, b)[0]
def forward ( self , covmat_sqrt , b ) : return self . cvxpylayer ( covmat_sqrt , b ) [ 0 ]