Convert an activation layer from mxnet to coreml.

    Parameters
    ----------
    network: net
        A mxnet network object.

    layer: node
        Node to convert.

    module: module
        An module for MXNet

    builder: NeuralNetworkBuilder
        A neural network builder object.

def convert_activation(net, node, module, builder):
    """Convert an activation layer from mxnet to coreml.

    Parameters
    ----------
    network: net
        A mxnet network object.

    layer: node
        Node to convert.

    module: module
        An module for MXNet

    builder: NeuralNetworkBuilder
        A neural network builder object.
    """
    input_name, output_name = _get_input_output_name(net, node)
    name = node['name']
    mx_non_linearity = _get_attrs(node)['act_type']
    #TODO add SCALED_TANH, SOFTPLUS, SOFTSIGN, SIGMOID_HARD, LEAKYRELU, PRELU, ELU, PARAMETRICSOFTPLUS, THRESHOLDEDRELU, LINEAR
    if mx_non_linearity == 'relu':
        non_linearity = 'RELU'
    elif mx_non_linearity == 'tanh':
        non_linearity = 'TANH'
    elif mx_non_linearity == 'sigmoid':
        non_linearity = 'SIGMOID'
    else:
        raise TypeError('Unknown activation type %s' % mx_non_linearity)
    builder.add_activation(name = name,
                           non_linearity = non_linearity,
                           input_name = input_name,
                           output_name = output_name)