training/models.py [786:809]:
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        input_feats = Variable()

        T = False
        if self.image_input == True:
            N, T, _ = img_feats.size()
            input_feats = img_feats

        if self.question_input == True:
            N, D = question_feats.size()
            question_feats = question_feats.view(N, 1, D)
            if T == False:
                T = actions_in.size(1)
            question_feats = question_feats.repeat(1, T, 1)
            if len(input_feats) == 0:
                input_feats = question_feats
            else:
                input_feats = torch.cat([input_feats, question_feats], 2)

        if self.action_input == True:
            if len(input_feats) == 0:
                input_feats = self.action_embed(actions_in)
            else:
                input_feats = torch.cat(
                    [input_feats, self.action_embed(actions_in)], 2)
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training/models.py [824:847]:
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        input_feats = Variable()

        T = False
        if self.image_input == True:
            N, T, _ = img_feats.size()
            input_feats = img_feats

        if self.question_input == True:
            N, D = question_feats.size()
            question_feats = question_feats.view(N, 1, D)
            if T == False:
                T = actions_in.size(1)
            question_feats = question_feats.repeat(1, T, 1)
            if len(input_feats) == 0:
                input_feats = question_feats
            else:
                input_feats = torch.cat([input_feats, question_feats], 2)

        if self.action_input == True:
            if len(input_feats) == 0:
                input_feats = self.action_embed(actions_in)
            else:
                input_feats = torch.cat(
                    [input_feats, self.action_embed(actions_in)], 2)
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