training/models.py [898:940]:
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            image_input=rnn_image_input,
            image_feat_dim=rnn_image_feat_dim,
            question_input=question_input,
            question_embed_dim=question_hidden_dim,
            action_input=rnn_action_input,
            action_embed_dim=rnn_action_embed_dim,
            num_actions=num_output,
            rnn_type=rnn_type,
            rnn_hidden_dim=rnn_hidden_dim,
            rnn_num_layers=rnn_num_layers,
            rnn_dropout=rnn_dropout)

    def forward(self,
                img_feats,
                questions,
                actions_in,
                action_lengths,
                hidden=False,
                step=False):
        N, T, _ = img_feats.size()

        # B x T x 128
        img_feats = self.cnn_fc_layer(img_feats)

        if self.question_input == True:
            ques_feats = self.q_rnn(questions)
            ques_feats = self.ques_tr(ques_feats)

            if step == True:
                output, hidden = self.nav_rnn.step_forward(
                    img_feats, ques_feats, actions_in, hidden)
            else:
                output, hidden = self.nav_rnn(img_feats, ques_feats,
                                              actions_in, action_lengths)
        else:
            if step == True:
                output, hidden = self.nav_rnn.step_forward(
                    img_feats, False, actions_in, hidden)
            else:
                output, hidden = self.nav_rnn(img_feats, False, actions_in,
                                              action_lengths)

        return output, hidden
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training/models.py [985:1027]:
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            image_input=rnn_image_input,
            image_feat_dim=rnn_image_feat_dim,
            question_input=question_input,
            question_embed_dim=question_hidden_dim,
            action_input=rnn_action_input,
            action_embed_dim=rnn_action_embed_dim,
            num_actions=num_output,
            rnn_type=rnn_type,
            rnn_hidden_dim=rnn_hidden_dim,
            rnn_num_layers=rnn_num_layers,
            rnn_dropout=rnn_dropout)

    def forward(self,
                img_feats,
                questions,
                actions_in,
                action_lengths,
                hidden=False,
                step=False):
        N, T, _ = img_feats.size()

        # B x T x 128
        img_feats = self.cnn_fc_layer(img_feats)

        if self.question_input == True:
            ques_feats = self.q_rnn(questions)
            ques_feats = self.ques_tr(ques_feats)

            if step == True:
                output, hidden = self.nav_rnn.step_forward(
                    img_feats, ques_feats, actions_in, hidden)
            else:
                output, hidden = self.nav_rnn(img_feats, ques_feats,
                                              actions_in, action_lengths)
        else:
            if step == True:
                output, hidden = self.nav_rnn.step_forward(
                    img_feats, False, actions_in, hidden)
            else:
                output, hidden = self.nav_rnn(img_feats, False, actions_in,
                                              action_lengths)

        return output, hidden
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