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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_leakyrelu(net, node, module, builder):
 """Convert a leakyrelu 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']
    inputs = node['inputs']
    args, _ = module.get_params()
    mx_non_linearity = _get_attrs(node)['act_type']
    if mx_non_linearity == 'elu':
        non_linearity = 'ELU'
        slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
        params = slope
    elif mx_non_linearity == 'leaky':
        non_linearity = 'LEAKYRELU'
        slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
        params = [slope]
    elif mx_non_linearity == 'prelu':
        non_linearity = 'PRELU'
        params = args[_get_node_name(net, inputs[1][0])].asnumpy()
    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,
                           params = params)