parameters: (node, **kwargs)
return_statement: return nodes
docstring: Map MXNet's sum operator attributes to onnx's ReduceSum operator
      and return the created node.
docstring_summary: Map MXNet's sum operator attributes to onnx's ReduceSum operator
      and return the created node.
docstring_tokens: Map MXNet s sum operator attributes to onnx s ReduceSum operator and return the created node .
function: def convert_sum(node, **kwargs):
      """Map MXNet's sum operator attributes to onnx's ReduceSum operator
      and return the created node.
      """
      from onnx.helper import make_node
  
      name, input_nodes, attrs = get_inputs(node, kwargs)
  
      mx_axis = attrs.get("axis", None)
      axes = convert_string_to_list(str(mx_axis)) if mx_axis not in [None, 'None'] else None
  
      keepdims = get_boolean_attribute_value(attrs, "keepdims")
  
      if axes:
          create_tensor(axes, name+'_axes', kwargs['initializer'])
          input_nodes.append(name+'_axes')
          node = make_node(
              'ReduceSum',
              inputs=input_nodes,
              outputs=[name],
              keepdims=keepdims,
              name=name
          )
          return [node]
      else:
          create_tensor([1], name+'_1', kwargs['initializer'])
          nodes = [
              onnx.helper.make_node(
                  'ReduceSum',
                  inputs=input_nodes,
                  outputs=[name],
                  keepdims=keepdims,
              )
          ]
      return nodes
function_tokens: def convert_sum ( node , * * kwargs ) : from onnx . helper import make_node name , input_nodes , attrs = get_inputs ( node , kwargs ) mx_axis = attrs . get ( "axis" , None ) axes = convert_string_to_list ( str ( mx_axis ) ) if mx_axis not in [ None , 'None' ] else None keepdims = get_boolean_attribute_value ( attrs , "keepdims" ) if axes : create_tensor ( axes , name + '_axes' , kwargs [ 'initializer' ] ) input_nodes . append ( name + '_axes' ) node = make_node ( 'ReduceSum' , inputs = input_nodes , outputs = [ name ] , keepdims = keepdims , name = name ) return [ node ] else : create_tensor ( [ 1 ] , name + '_1' , kwargs [ 'initializer' ] ) nodes = [ onnx . helper . make_node ( 'ReduceSum' , inputs = input_nodes , outputs = [ name ] , keepdims = keepdims , ) ] return nodes