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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_upsample(net, node, module, builder):
 """Convert a UpSampling layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer 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']
    param = _get_attr(node)
    inputs = node['inputs']
    args, _ = module.get_params()

    scale = literal_eval(param['scale'])

    #method
    if 'sample_type' in param.keys():
        method = param['sample_type']
        if method == 'nearest':
            mode = 'NN'
        elif method == '':
            mode = 'BILINEAR'

    builder.add_upsample(name, scaling_factor_h=scale, scaling_factor_w=scale,
                         input_name=input_name, output_name=output_name, mode=mode)