for the biaswe use small constant value to ensure that the tensors activate in the intial stages and therefore contribute to the propagation the weights and bias tensors are stored in dictionary objects for ease of access add this code to your file to define the biasesmain py biases ' 'tf variable(tf constant( shape=[n_hidden ]))' 'tf variable(tf constant( shape=[n_hidden ]))' 'tf variable(tf constant( shape=[n_hidden ]))'out'tf variable(tf constant( shape=[n_output])nextset up the layers of the network by defining the operations that will manipulate the tensors add these lines to your filemain py layer_ tf add(tf matmul(xweights[' '])biases[' ']layer_ tf add(tf matmul(layer_ weights[' '])biases[' ']layer_ tf add(tf matmul(layer_ weights[' '])biases[' ']layer_drop tf nn dropout(layer_ keep_proboutput_layer tf matmul(layer_ weights['out']biases['out'