<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def convert_embedding(net, node, model, builder):
 """Convert an embedding layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer Node to convert. model: model An model 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']
    outputs = node['outputs']
    arg_params, aux_params = model.get_params()
    W = arg_params[_get_node_name(net, inputs[1][0])].asnumpy()
    if not ONE_HOT_ENCODE_HACK:
        nC, nB = W.shape
        W = W.T
        builder.add_embedding(name = name,
                              W = W,
                              b = None,
                              input_dim = nC,
                              output_channels = nB,
                              has_bias = False,
                              input_name = input_name,
                              output_name = output_name)
    else:
        W = W.T
        nC, nB = W.shape
        builder.add_inner_product(name = name,
                W = W,
                b = None,
                input_channels = nB,
                output_channels = nC,
                has_bias = False,
                input_name = input_name,
                output_name = output_name)