code: def torch_dim_inference_rule(n: Node, symbols, constraints, counter):
      assert isinstance(n.args[0], Node)
      my_dim, counter = gen_dvar(counter)
      symbols[n] = my_dim
      input = symbols[n.args[0]]
  
      input_dyn = BinConstraintT(input, Dyn, op_eq)
      output_dyn = BinConstraintD(my_dim, Dyn, op_eq)
  
      c1 = []
  
      for i in range(1, MAX_TENSOR_RANK + 1):
          new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
  
          c_tensor_i = Conj(
              [
                  BinConstraintT(
                      input, TensorType(new_dims_rhs_1), op_eq
                  ),
                  BinConstraintD(my_dim, i, op_eq),
              ]
          )
          c1.append(c_tensor_i)
  
      return [Disj([Conj([input_dyn, output_dyn]), Disj(c1)])], 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 35 for calling a global function with example usage: torch_dim_inference_rule(n, symbols, constraints, counter) and returns: list,  counter