def forward(self, x: torch.FloatTensor) -> Tuple[(D.Categorical, torch.FloatTensor)]:
    'Forward pass of the model.\n\n        Parameters\n        ----------\n        x : torch.FloatTensor\n            Input state tensor.\n\n        Returns\n        -------\n        Tuple[Normallike, torch.FloatTensor]\n            Normal or squashed Normal distribution (dist), State value estimate (V_hat).\n        '
    (pi_logits, V_hat) = self._get_dist_params(x)
    dist = D.Categorical(logits=pi_logits)
    return (dist, V_hat)
Forward pass of the model.

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

Returns
-------
Tuple[Normallike, torch.FloatTensor]
    Normal or squashed Normal distribution (dist), State value estimate (V_hat).
def forward(self, x: torch.FloatTensor) -> Tuple[(D.Categorical, torch.FloatTensor)]:
    'Forward pass of the model.\n\n        Parameters\n        ----------\n        x : torch.FloatTensor\n            Input state tensor.\n\n        Returns\n        -------\n        Tuple[Normallike, torch.FloatTensor]\n            Normal or squashed Normal distribution (dist), State value estimate (V_hat).\n        '
    (pi_logits, V_hat) = self._get_dist_params(x)
    dist = D.Categorical(logits=pi_logits)
    return (dist, V_hat)