code: def operator_sum(self: Variable, name: Optional[str]) -> "Variable":
      r = Variable(torch.sum(self.value), name=name)
      # print(f'{r.name} = {self.name}.sum()')
  
      def propagate(dL_doutputs: List[Variable]):
          (dL_dr,) = dL_doutputs
          size = self.value.size()
          return [dL_dr.expand(*size)]
  
      gradient_tape.append(
          TapeEntry(
              inputs=[self.name], outputs=[r.name], propagate=propagate
          )
      )
      return r
  
imports: from typing import Callable, Dict, List, NamedTuple, Optional
  import torch
  import torch._dynamo
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.testing import CompileCounter, same
label_desc: Use image node_id 6 for calling a global function with example usage: operator_sum(name) and returns: r, list