minihack/agent/polybeast/models/base.py [481:531]:
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        assert crop_rep.shape[0] == T * B

        reps.append(crop_rep)

        # -- [B x H x W x K]
        glyphs_emb = self.glyph_embedding(glyphs)
        # glyphs_emb = self.embed(glyphs)
        # -- [B x K x W x H]
        glyphs_emb = glyphs_emb.transpose(1, 3)
        # -- [B x W x H x K]
        glyphs_rep = self.extract_representation(glyphs_emb)

        # -- [B x K']
        glyphs_rep = glyphs_rep.view(T * B, -1)
        if self.equalize_input_dim:
            glyphs_rep = self.project_glyph_dim(glyphs_rep)

        assert glyphs_rep.shape[0] == T * B

        # -- [B x K'']
        reps.append(glyphs_rep)

        # MESSAGING MODEL
        if self.msg_model != "none":
            # [T x B x 256] -> [T * B x 256]
            messages = inputs["message"].long().view(T * B, -1)
            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/polybeast/models/intrinsic.py [259:308]:
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        assert crop_rep.shape[0] == T * B

        reps.append(crop_rep)

        # -- [B x H x W x K]
        glyphs_emb = self.glyph_embedding(glyphs)
        # -- [B x K x W x H]
        glyphs_emb = glyphs_emb.transpose(1, 3)
        # -- [B x W x H x K]
        glyphs_rep = self.extract_representation(glyphs_emb)

        # -- [B x K']
        glyphs_rep = glyphs_rep.view(T * B, -1)
        if self.equalize_input_dim:
            glyphs_rep = self.project_glyph_dim(glyphs_rep)

        assert glyphs_rep.shape[0] == T * B

        # -- [B x K'']
        reps.append(glyphs_rep)

        # MESSAGING MODEL
        if self.msg_model != "none":
            # [T x B x 256] -> [T * B x 256]
            messages = inputs["message"].long().view(T * B, -1)
            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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