code: def convert_embedding(net, node, model, builder):
      
      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)
docs: 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.