code: def generate_all_int_dyn_dim_possibilities(my_list: List[DVar]):
      """
      Generate all possibilities of being equal or not equal to dyn for my_list
      Args:
          my_list: List of tensor dimensions
  
      Returns: A list of a list of constraints. Each list of constraints corresponds to
      one possibility about the values of the dimension variables
      """
      # generate all possibilities of being equal or not equal to dyn for my_list
      eq_possibilities = [
          BinConstraintD(my_list[i], Dyn, op_eq)
          for i in range(len(my_list))
      ]
      neq_possibilities = [
          BinConstraintD(my_list[i], Dyn, op_neq)
          for i in range(len(my_list))
      ]
      d_possibilities = []
  
      for i in zip(eq_possibilities, neq_possibilities):
          d_possibilities.append(list(i))
      all_possibilities = list(itertools.product(*d_possibilities))
      return all_possibilities
  
imports: import copy
  import itertools
  from torch.fx.experimental.migrate_gradual_types.constraint_generator import BinConstraintT, MAX_TENSOR_RANK
  from torch.fx.experimental.migrate_gradual_types.constraint import T, BinConstraintD, Conj, Constraint, DVar, TVar, Transpose
  from torch.fx.experimental.migrate_gradual_types.constraint import Disj, TGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcConv, CalcMaxPool
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcProduct, CanReshape
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, Prod, F, GetItem, GetItemTensor, IndexSelect
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_precision, op_leq, op_matching
  from torch.fx.experimental.migrate_gradual_types.operation import op_consistency, op_neq
  from torch.fx.experimental.migrate_gradual_types.operation import op_mul, op_add, op_sub, op_div, op_mod
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar
  from torch.fx.tensor_type import TensorType, Dyn
  from typing import Callable, Dict, List
label_desc: Use image node_id 21 for calling a global function with example usage: generate_all_int_dyn_dim_possibilities(my_list) and returns: all_possibilities