correcting the layers

import deepchem as dc

import tensorflow as tensorflow

from deepchem.models.tensorgraph.layers import Layer, Input, Dense, Hingeloss, Label, Feature

from deepchem.models.tensorgraph.layers import ReduceMean, InputFifoQueue, ReLU

from deepchem.models.tensorgraph.layers import ReduceMean, InputFifoQueue, ReLU, Add

from deepchem.models.tensorgraph.tensor_graph import TensorGraph

class Scscore(TensorGraph):

      out_channels=300, in_layers=[dense_reactant_4], activation_fn=tf.nn.relu)

  dense_product_5 = dense_reactant_5.shared(in_layers=[dense_product_4])

  output_dense = Add(in_layers=[dense_reactant_5, dense_product_5])

  output = Sigmoid(in_layers=[output_dense])

  output_reactant = Sigmoid(in_layers=[dense_reactant_5])

  output_product = Sigmoid(in_layers=[dense_product_5])

  output = tf.subtract(output_product, output_reactant)

  tg.add_output(output)

  label = Label(shape=(None, 1))

  loss = Hingeloss(in_layers=[label, output])

  loss = Hingeloss(in_layers=[output_product, output_reactant])

  tg.set_loss(loss)