code: def add_inference_rule(n: Node):
      """
      Apply the addition inference rule. This includes:
      - scalar addition
      - broadcasting semantics
  
      Note that we always return the least precise type between
      the operands (after applying broadcasting) to be the final type of the operation
  
      Note that we do not modify the operand types themselves after applying broadcasting
      to them. We only use them to calculate the final type
      """
      assert isinstance(n.args[0], Node)
      assert isinstance(n.args[1], Node)
      t1 = n.args[0].type
      t2 = n.args[1].type
  
      # handle scalar addition
      if t1 == int and isinstance(t2, TensorType):
          n.type = t2
          return n.type
  
      # handle scalar addition
      elif t2 == int and isinstance(t1, TensorType):
          n.type = t1
          return n.type
  
      # we bring the new types to the point where
      # we can check for consistency
      # any inconsistency would not have been caused
      # by broadcasting at this point
      (new_t1, new_t2) = broadcast_types(t1, t2)
  
      if new_t1 != t1 or new_t2 != t2:
          n.meta["broadcast"] = True
          n.meta[str(n.args[0])] = new_t1
          n.meta[str(n.args[1])] = new_t2
  
      else:
          n.meta["broadcast"] = False
  
      new_t1 = t1 if not n.meta["broadcast"] else new_t1
      new_t2 = t2 if not n.meta["broadcast"] else new_t2
  
      # we check for consistency between the new types
      if is_consistent(new_t1, new_t2):
          # we return the less precise type because
          # broadcasting may have happened
          # for operands with shape [1,2,Dyn] and [1,2,1]
          # we have to assign the node [1,2,Dyn]
          if is_more_precise(new_t1, new_t2):
              n.type = new_t2
          else:
              n.type = new_t1
          return n.type
      else:
          raise TypeError(
              f"Cannot add arguments {n.args[0]} ({ n.args[0].type}) and {n.args[1]} ({ n.args[1].type}) in node {n}."
              f" Types should match "
          )
  
imports: from functools import reduce
  import torch
  import operator
  from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
  from typing import Callable, Dict
  from torch.fx.node import Target, Node
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.conv import Conv2d
  from torch.fx.experimental.refinement_types import Equality
  import itertools
  from torch.fx.experimental.unification import Var
  import sympy
label_desc: Use image node_id 6 for calling a global function with example usage: add_inference_rule(n) and returns: n, n, n