def build_mlp(input_placeholder, output_size, scope, n_layers, size, activation=tf.tanh, output_activation=None):
    """
        Builds a feedforward neural network

        arguments:
            input_placeholder: placeholder variable for the state (batch_size, input_size)
            output_size: size of the output layer
            scope: variable scope of the network
            n_layers: number of hidden layers
            size: dimension of the hidden layer
            activation: activation of the hidden layers
            output_activation: activation of the ouput layers
        returns:
            output placeholder of the network (the result of a forward pass)
        Hint: use tf.layers.dense
    """

#     with tf.variable_scope(scope):
#         layers = tf_layers.stack(input_placeholder, tf_layers.fully_connected, [size] * (n_layers - 1),
#                        scope = 'fc', activation_fn = activation)

#         output_placeholder = tf_layers.fully_connected(layers, output_size, activation_fn = output_activation)


    out = input_placeholder
        for l in range(n_layers-1):
            out = tf.layers.dense(inputs=out, units=size, activation=activation)
        out = tf.layers.dense(inputs=out, units=output_size, activation=output_activation)
        return out

    return output_placeholder