def __init__()

in training/models.py [0:0]


    def __init__(self,
                 question_vocab,
                 num_output=4,
                 question_wordvec_dim=64,
                 question_hidden_dim=64,
                 question_num_layers=2,
                 question_dropout=0.5,
                 planner_rnn_image_feat_dim=128,
                 planner_rnn_action_embed_dim=32,
                 planner_rnn_type='GRU',
                 planner_rnn_hidden_dim=1024,
                 planner_rnn_num_layers=1,
                 planner_rnn_dropout=0,
                 controller_fc_dims=(256, )):
        super(NavPlannerControllerModel, self).__init__()

        self.cnn_fc_layer = nn.Sequential(
            nn.Linear(32 * 10 * 10, planner_rnn_image_feat_dim),
            nn.ReLU(),
            nn.Dropout(p=0.5))

        q_rnn_kwargs = {
            'token_to_idx': question_vocab['questionTokenToIdx'],
            'wordvec_dim': question_wordvec_dim,
            'rnn_dim': question_hidden_dim,
            'rnn_num_layers': question_num_layers,
            'rnn_dropout': question_dropout,
        }
        self.q_rnn = QuestionLstmEncoder(**q_rnn_kwargs)
        self.ques_tr = nn.Sequential(
            nn.Linear(question_hidden_dim, question_hidden_dim),
            nn.ReLU(),
            nn.Dropout(p=0.5))

        self.planner_nav_rnn = NavRnn(
            image_input=True,
            image_feat_dim=planner_rnn_image_feat_dim,
            question_input=True,
            question_embed_dim=question_hidden_dim,
            action_input=True,
            action_embed_dim=planner_rnn_action_embed_dim,
            num_actions=num_output,
            rnn_type=planner_rnn_type,
            rnn_hidden_dim=planner_rnn_hidden_dim,
            rnn_num_layers=planner_rnn_num_layers,
            rnn_dropout=planner_rnn_dropout,
            return_states=True)

        controller_kwargs = {
            'input_dim':
            planner_rnn_image_feat_dim + planner_rnn_action_embed_dim +
            planner_rnn_hidden_dim,
            'hidden_dims':
            controller_fc_dims,
            'output_dim':
            2,
            'add_sigmoid':
            0
        }
        self.controller = build_mlp(**controller_kwargs)