src/run.py [93:112]:
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    load_data = {}
    assert opts.load_path is None or opts.resume is None, "Only one of load path and resume can be given"
    load_path = opts.load_path if opts.load_path is not None else opts.resume
    if load_path is not None:
        print('\nLoading data from {}'.format(load_path))
        load_data = torch_load_cpu(load_path)

    # Initialize model
    model_class = {
        'attention': AttentionModel,
        'nar': NARModel,
        # 'pointer': PointerNetwork
    }.get(opts.model, None)
    assert model_class is not None, "Unknown model: {}".format(model_class)
    encoder_class = {
        'gnn': GNNEncoder,
        'gat': GraphAttentionEncoder,
        'mlp': MLPEncoder
    }.get(opts.encoder, None)
    assert encoder_class is not None, "Unknown encoder: {}".format(encoder_class)
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src/run.py [294:313]:
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    load_data = {}
    assert opts.load_path is None or opts.resume is None, "Only one of load path and resume can be given"
    load_path = opts.load_path if opts.load_path is not None else opts.resume
    if load_path is not None:
        print('\nLoading data from {}'.format(load_path))
        load_data = torch_load_cpu(load_path)

    # Initialize model
    model_class = {
        'attention': AttentionModel,
        'nar': NARModel,
        # 'pointer': PointerNetwork
    }.get(opts.model, None)
    assert model_class is not None, "Unknown model: {}".format(model_class)
    encoder_class = {
        'gnn': GNNEncoder,
        'gat': GraphAttentionEncoder,
        'mlp': MLPEncoder
    }.get(opts.encoder, None)
    assert encoder_class is not None, "Unknown encoder: {}".format(encoder_class)
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