code: def full_inference_rule(n: Node, symbols, constraints, counter):
      full, counter = gen_tvar(counter)
      symbols[n] = full
      res = []
  
      assert isinstance(n.args[0], Iterable)
      for arg in n.args[0]:
          dim = arg if isinstance(arg, int) else symbols[arg]
          res.append(dim)
      c = BinConstraintT(full, TensorType(list(res)), op_eq)  # type: ignore[arg-type]
      return [c], 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 25 for calling a global function with example usage: full_inference_rule(n, symbols, constraints, counter) and returns: list,  counter