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Solve the following python programming problem given the following code:
# Execute for Theano / Lasagne version
raise Exception('Use the TensorFlow version!')

import theano
import lasagne

def build_cnn_theano_lasagne(input_var, features_shape):
    # Create a CNN of two convolution layers and 
    #   a fully-connected hidden layer in front of the output 'action score' layer
    
    lasagne.random.set_rng( np.random )

    # input layer
    network = lasagne.layers.InputLayer(
        shape=(None, features_shape[0], features_shape[1], features_shape[2]), 
        input_var=input_var)

    # Two convolutional layers (no dropout, no pooling)
    network = lasagne.layers.Conv2DLayer(
      network, num_filters=32, filter_size=(3,3),
      nonlinearity=lasagne.nonlinearities.rectify,
      W=lasagne.init.GlorotUniform(),
    )
    
    network = lasagne.layers.Conv2DLayer(
      network, num_filters=16, filter_size=(3,3),
      nonlinearity=lasagne.nonlinearities.rectify,
    )

    # Two fully-connected layers - leading to ONE output value : the Q(features(board))
    network = lasagne.layers.DenseLayer(
      network, num_units=32,
      nonlinearity=lasagne.nonlinearities.rectify,
    )

    network = lasagne.layers.DenseLayer(
      network, num_units=1,
      nonlinearity=lasagne.nonlinearities.linear,
    )

    return network

class rl_model_theano_lasagne:
  is_tensorflow=False
  def __init__(self, features_shape):
    board_input = theano.tensor.tensor4('inputs')
    board_score = theano.tensor.vector('targets')

    np.random.seed(0) # This is for the initialisation inside the CNN
    cnn = build_cnn_theano_lasagne(board_input, features_shape)
    self.network = cnn
    
    # This is for running the model (training, etc)
    estimate_q_value = lasagne.layers.get_output(cnn)  # 'running'
    self.evaluate_features = theano.function([board_input], estimate_q_value)

    # This is for repeatedly testing the model (deterministic)
    predict_q_value  = lasagne.layers.get_output(cnn, deterministic=True)
    self.evaluate_features_deterministic =...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Build the Model to Evaluate the Board's Features