code: def type_inference_rule(n: Node, symbols, constraints, counter):
      """
      We generate the constraint: input = output
      """
      assert isinstance(n.args[0], Node)
      assert isinstance(n.args[1], Node)
  
      output, counter = gen_tvar(counter)
      symbols[n] = output
  
      from_arg = symbols[n.args[0]]
      to_arg = symbols[n.args[1]]
  
      assert isinstance(from_arg, TVar)
      assert isinstance(to_arg, TVar)
  
      return [
          BinConstraintT(from_arg, to_arg, op_consistency),
          BinConstraintT(output, to_arg, op_eq),
      ], counter
  
imports: import torch
  import operator
  import warnings
  from typing import Callable, Dict, Iterable
  from torch.fx._symbolic_trace import _assert_is_none
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, CalcProduct, Disj, TGreatestUpperBound, CalcMaxPool, CalcConv, Conj, BinConstraintT, CanReshape, BinConstraintD, GetItem, T, F, TVar, DVar, GetItemTensor, IndexSelect, Transpose, DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_matching, op_consistency, op_leq, op_precision, op_gt, op_div, op_sub, op_neq, op_lt, op_add, op_mul
  from torch.fx.node import Target, Node
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar, gen_tvar, gen_bvar
  from torch.fx.tensor_type import Dyn, TensorType
  from torch.nn.modules.conv import Conv2d
  from torch.nn.modules.batchnorm import BatchNorm2d
label_desc: Use image node_id 9 for calling a global function with example usage: type_inference_rule(n, symbols, constraints, counter) and returns: list,  counter