(self, pub_obses, range_idxs, legal_action_masks)
return y * legal_action_masks
Args:
            pub_obses (list):                       list of np arrays of shape [np.arr([history_len, n_features]), ...)
            range_idxs (torch.Tensor):              integer representation of hand
Args:
            pub_obses (list):                       list of np arrays of shape [np.arr([history_len, n_features]), ...)
            range_idxs (torch.Tensor):              integer representation of hand
Args : pub_obses ( list ) : list of np arrays of shape [ np . arr ( [ history_len n_features ] ) ... ) range_idxs ( torch . Tensor ) : integer representation of hand
def forward(self, pub_obses, range_idxs, legal_action_masks):
        """
        Args:
            pub_obses (list):                       list of np arrays of shape [np.arr([history_len, n_features]), ...)
            range_idxs (torch.Tensor):              integer representation of hand
        """
        y = self._mpm(pub_obses=pub_obses, range_idxs=range_idxs)
        y = self._relu(self._final_layer(y))
        y = self._val(y)

        # The mask is important because the strategy inference assumes illegals to have exactly zero
        return y * legal_action_masks
def forward ( self , pub_obses , range_idxs , legal_action_masks ) : y = self . _mpm ( pub_obses = pub_obses , range_idxs = range_idxs ) y = self . _relu ( self . _final_layer ( y ) ) y = self . _val ( y ) # The mask is important because the strategy inference assumes illegals to have exactly zero return y * legal_action_masks