code: def convert_embedding(builder, layer, input_names, output_names, keras_layer):
      
      # Get input and output names
      input_name, output_name = (input_names[0], output_names[0])
  
      # Get the weights from keras
      W = keras_layer.get_weights ()[0].T
  
      # assuming keras embedding layers don't have biases
      builder.add_embedding(name = layer,
                            W = W,
                            b = None,
                            input_dim = keras_layer.input_dim,
                            output_channels = keras_layer.output_dim,
                            has_bias = False,
                            input_name = input_name,
                            output_name = output_name)
docs: Convert a dense layer from keras to coreml.
  
      Parameters
      keras_layer: layer
      ----------
          A keras layer object.
  
      builder: NeuralNetworkBuilder
          A neural network builder object.