def forward(self, x: torch.FloatTensor) -> Tuple[(torch.FloatTensor, torch.FloatTensor, torch.FloatTensor)]:
    'Returns the learned paremters of the distribution.\n\n        Parameters\n        ----------\n        x : torch.FloatTensor\n            Input state tensor.\n\n        Returns\n        -------\n        Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]\n            Alpha parameter (alpha), Beta parameter (beta), State value estimate (V_hat).\n        '
    x = self.trunk(x)
    V_hat = self.value_head(x)
    dist_params = self.dist_head(x)
    dist_params = torch.clamp(dist_params, min=self.log_param_min, max=self.log_param_max)
    (alpha, beta) = dist_params.exp().chunk(2, dim=(- 1))
    return (alpha, beta, V_hat)
Returns the learned paremters of the distribution.

Parameters
----------
x : torch.FloatTensor
    Input state tensor.

Returns
-------
Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]
    Alpha parameter (alpha), Beta parameter (beta), State value estimate (V_hat).
def forward(self, x: torch.FloatTensor) -> Tuple[(torch.FloatTensor, torch.FloatTensor, torch.FloatTensor)]:
    'Returns the learned paremters of the distribution.\n\n        Parameters\n        ----------\n        x : torch.FloatTensor\n            Input state tensor.\n\n        Returns\n        -------\n        Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]\n            Alpha parameter (alpha), Beta parameter (beta), State value estimate (V_hat).\n        '
    x = self.trunk(x)
    V_hat = self.value_head(x)
    dist_params = self.dist_head(x)
    dist_params = torch.clamp(dist_params, min=self.log_param_min, max=self.log_param_max)
    (alpha, beta) = dist_params.exp().chunk(2, dim=(- 1))
    return (alpha, beta, V_hat)