minihack/agent/polybeast/models/base.py [507:531]:
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            if self.msg_model == "cnn":
                # convert messages to one-hot, [T * B x 96 x 256]
                one_hot = F.one_hot(messages, num_classes=NUM_CHARS).transpose(
                    1, 2
                )
                char_rep = self.conv2_6_fc(self.conv1(one_hot.float()))
            elif self.msg_model == "lt_cnn":
                # [ T * B x E x 256 ]
                char_emb = self.char_lt(messages).transpose(1, 2)
                char_rep = self.conv2_6_fc(self.conv1(char_emb))
            else:  # lstm, gru
                char_emb = self.char_lt(messages)
                output = self.char_rnn(char_emb)[0]
                fwd_rep = output[:, -1, : self.h_dim // 2]
                bwd_rep = output[:, 0, self.h_dim // 2 :]
                char_rep = torch.cat([fwd_rep, bwd_rep], dim=1)

            if self.equalize_input_dim:
                char_rep = self.project_msg_dim(char_rep)
            reps.append(char_rep)

        st = torch.cat(reps, dim=1)

        # -- [B x K]
        st = self.fc(st)
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minihack/agent/rllib/models.py [482:506]:
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            if self.msg_model == "cnn":
                # convert messages to one-hot, [B x 96 x 256]
                one_hot = F.one_hot(messages, num_classes=NUM_CHARS).transpose(
                    1, 2
                )
                char_rep = self.conv2_6_fc(self.conv1(one_hot.float()))
            elif self.msg_model == "lt_cnn":
                # [B x E x 256 ]
                char_emb = self.char_lt(messages).transpose(1, 2)
                char_rep = self.conv2_6_fc(self.conv1(char_emb))
            else:  # lstm, gru
                char_emb = self.char_lt(messages)
                output = self.char_rnn(char_emb)[0]
                fwd_rep = output[:, -1, : self.h_dim // 2]
                bwd_rep = output[:, 0, self.h_dim // 2 :]
                char_rep = torch.cat([fwd_rep, bwd_rep], dim=1)

            if self.equalize_input_dim:
                char_rep = self.project_msg_dim(char_rep)
            reps.append(char_rep)

        st = torch.cat(reps, dim=1)

        # -- [B x K]
        st = self.fc(st)
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