code: def convert_dense(net, node, module, builder):
      
      input_name, output_name = _get_input_output_name(net, node)
      has_bias = True
      name = node['name']
  
      inputs = node['inputs']
      args, _ = module.get_params()
      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
      nC, nB = W.shape
  
      builder.add_inner_product(
          name=name,
          W=W,
          b=Wb,
          input_channels=nB,
          output_channels=nC,
          has_bias=has_bias,
          input_name=input_name,
          output_name=output_name
      )
docs: Convert a dense 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.