def __init__()

in training/models.py [0:0]


    def __init__(self,
                 image_input=False,
                 image_feat_dim=128,
                 question_input=False,
                 question_embed_dim=128,
                 action_input=False,
                 action_embed_dim=32,
                 num_actions=4,
                 mode='sl',
                 rnn_type='LSTM',
                 rnn_hidden_dim=128,
                 rnn_num_layers=2,
                 rnn_dropout=0,
                 return_states=False):
        super(NavRnn, self).__init__()

        self.image_input = image_input
        self.image_feat_dim = image_feat_dim

        self.question_input = question_input
        self.question_embed_dim = question_embed_dim

        self.action_input = action_input
        self.action_embed_dim = action_embed_dim

        self.num_actions = num_actions

        self.rnn_type = rnn_type
        self.rnn_hidden_dim = rnn_hidden_dim
        self.rnn_num_layers = rnn_num_layers

        self.return_states = return_states

        rnn_input_dim = 0
        if self.image_input == True:
            rnn_input_dim += image_feat_dim
            print('Adding input to %s: image, rnn dim: %d' % (self.rnn_type,
                                                              rnn_input_dim))

        if self.question_input == True:
            rnn_input_dim += question_embed_dim
            print('Adding input to %s: question, rnn dim: %d' %
                  (self.rnn_type, rnn_input_dim))

        if self.action_input == True:
            self.action_embed = nn.Embedding(num_actions, action_embed_dim)
            rnn_input_dim += action_embed_dim
            print('Adding input to %s: action, rnn dim: %d' % (self.rnn_type,
                                                               rnn_input_dim))

        self.rnn = getattr(nn, self.rnn_type)(
            rnn_input_dim,
            self.rnn_hidden_dim,
            self.rnn_num_layers,
            dropout=rnn_dropout,
            batch_first=True)
        print('Building %s with hidden dim: %d' % (self.rnn_type,
                                                   rnn_hidden_dim))

        self.decoder = nn.Linear(self.rnn_hidden_dim, self.num_actions)