more dead code

n_feat = 75

# Batch size of models

batch_size = 50

graph_model = dc.nn.SequentialGraph(n_feat)

graph_model.add(dc.nn.GraphConv(64, n_feat, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphPool())

graph_model.add(dc.nn.GraphConv(64, 64, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphPool())

# Gather Projection

graph_model.add(dc.nn.Dense(128, 64, activation='relu'))

graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))

model = dc.models.MultitaskGraphRegressor(

    graph_model,

    len(hopv_tasks),

    n_feat,

    batch_size=batch_size,

    learning_rate=1e-3,

    learning_rate_decay_time=1000,

    optimizer_type="adam",

    beta1=.9,

    beta2=.999)

model = GraphConvTensorGraph(

    len(hopv_tasks), batch_size=batch_size, mode='regression')