code: def index_select_inference_rule(
      n: Node, symbols, constraints, counter
  ):
      """
      We constrain the second argument to a vector or Dyn.
      The output replaces the input with the shape of the vector
      at the position given by the index (first argument)
      """
      # print(n.args)
      assert isinstance(n.args[0], Node)
      assert isinstance(n.args[1], int)
      assert isinstance(n.args[2], Node)
  
      index_select, counter = gen_tvar(counter)
      symbols[n] = index_select
  
      dims, counter = gen_tensor_dims(1, counter)
  
      # equality constraint
      is_size_1 = BinConstraintT(
          symbols[n.args[2]], TensorType(dims), op_eq
      )
      is_dyn = BinConstraintT(symbols[n.args[2]], Dyn, op_eq)
  
      c2 = Conj(
          [
              is_size_1,
              Disj(
                  [
                      IndexSelect(
                          i + 1,
                          symbols[n.args[0]],
                          dims[0],
                          n.args[1],
                          index_select,
                      )
                      for i in range(MAX_TENSOR_RANK)
                  ]
              ),
          ]
      )
      c3 = Conj(
          [
              is_dyn,
              Disj(
                  [
                      IndexSelect(
                          i + 1,
                          symbols[n.args[0]],
                          Dyn,
                          n.args[1],
                          index_select,
                      )
                      for i in range(MAX_TENSOR_RANK)
                  ]
              ),
          ]
      )
  
      return [Disj([c2, c3])], 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 5 for calling a global function with example usage: index_select_inference_rule(n, symbols, constraints, counter) and returns: list,  counter