code: def size_inference_rule(n: Node, symbols, constraints, counter):
      """
      The constraint is just lhs = rhs.
      Ex: size = input_ids.size()
      """
  
      if len(n.args) == 1:
          # generate the new variable
          size, counter = gen_tvar(counter)
          symbols[n] = size
          input = symbols[n.args[0]]
          c = BinConstraintT(input, size, op_eq)
          return [c], counter
  
      elif len(n.args) == 2:
          # TODO: review this rule; should input = dyn; output = dyn be included here?
          if isinstance(n.args[1], int):
              # generate the new variable
              size_index, counter = gen_dvar(counter)
              symbols[n] = size_index
              input = symbols[n.args[0]]
              c2 = [
                  GetItem(i + 1, n.args[1], size_index, input)
                  for i in range(MAX_TENSOR_RANK)
              ]
              c3 = BinConstraintD(0, size_index, op_leq)
  
              input_dyn = BinConstraintT(input, Dyn, op_eq)
              output_dyn = BinConstraintD(size_index, Dyn, op_eq)
              c1 = Conj([input_dyn, output_dyn])
  
              return [Disj([c1, Conj([Disj(c2), c3])])], counter
  
          else:
              raise NotImplementedError
  
      else:
          raise NotImplementedError
  
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 16 for calling a global function with example usage: size_inference_rule(n, symbols, constraints, counter) and returns: list,  counter, list,  counter