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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_convolution(net, node, module, builder):
 """Convert a convolution 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']
    param = _get_attrs(node)
    inputs = node['inputs']
    args, _ = module.get_params()

    if 'no_bias' in param.keys():
        has_bias = not literal_eval(param['no_bias'])
    else:
        has_bias = True

    if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
        pad = literal_eval(param['pad'])
        builder.add_padding(
            name=name+"_pad",
            left=pad[1],
            right=pad[1],
            top=pad[0],
            bottom=pad[0],
            value=0,
            input_name=input_name,
            output_name=name+"_pad_output")
        input_name = name+"_pad_output"

    border_mode = "valid"

    n_filters = int(param['num_filter'])
    n_groups = int(param['num_group']) if 'num_group' in param else 1

    W = args[_get_node_name(net, inputs[1][0])].asnumpy()
    if has_bias:
        Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
    else:
        Wb = None

    channels = W.shape[1]

    stride_height = 1
    stride_width = 1
    if 'stride' in param.keys():
        stride_height, stride_width = literal_eval(param['stride'])

    kernel_height, kernel_width = literal_eval(param['kernel'])

    W = W.transpose((2, 3, 1, 0))
    builder.add_convolution(
        name=name,
        kernel_channels=channels,
        output_channels=n_filters,
        height=kernel_height,
        width=kernel_width,
        stride_height=stride_height,
        stride_width=stride_width,
        border_mode=border_mode,
        groups=n_groups,
        W=W,
        b=Wb,
        has_bias=has_bias,
        is_deconv=False,
        output_shape=None,
        input_name=input_name,
        output_name=output_name)