code: def nn(input, layers_sizes, reuse=None, flatten=False, name=""):
      
      for i, size in enumerate(layers_sizes):
          activation = tf.nn.relu if i < len(layers_sizes) - 1 else None
          input = tf.layers.dense(inputs=input,
                                  units=size,
                                  kernel_initializer=tf.contrib.layers.xavier_initializer(),
                                  reuse=reuse,
                                  name=name + '_' + str(i))
          if activation:
              input = activation(input)
      if flatten:
          assert layers_sizes[-1] == 1
          input = tf.reshape(input, [-1])
      return input
docs: Creates a simple neural network