code: def convert_upsample(net, node, module, builder):
      
      input_name, output_name = _get_input_output_name(net, node)
      name = node['name']
      param = _get_attr(node)
      inputs = node['inputs']
      args, _ = module.get_params()
  
      scale = literal_eval(param['scale'])
  
      #method
      if 'sample_type' in param.keys():
          method = param['sample_type']
          if method == 'nearest':
              mode = 'NN'
          elif method == '':
              mode = 'BILINEAR'
  
      builder.add_upsample(name, scaling_factor_h=scale, scaling_factor_w=scale,
                           input_name=input_name, output_name=output_name, mode=mode)
docs: Convert a UpSampling 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.