code: def operator_add(self: Variable, rhs: Variable) -> Variable:
      # Add follows a similar pattern to Mul, but it doesn't end up
      # capturing any variables.
      r = Variable(self.value + rhs.value)
      # print(f'{r.name} = {self.name} + {rhs.name}')
  
      def propagate(dL_doutputs: List[Variable]):
          (dL_dr,) = dL_doutputs
          dr_dself = 1.0
          dr_drhs = 1.0
          dL_dself = dL_dr * dr_dself
          dL_drhs = dL_dr * dr_drhs
          return [dL_dself, dL_drhs]
  
      gradient_tape.append(
          TapeEntry(
              inputs=[self.name, rhs.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 5 for calling a global function with example usage: operator_add(rhs) and returns: r, list