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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)