code: def gen_all_reshape_possibilities(list_of_dims, target):
      """
      Consider all possibilities what the input dimensions could be (number or dynamic)
      Then generate the appropriate constraints using multiplication or mod depending on the possibility
      The possibilities we consider here are the cross product of being equal to dyn or not equal to dyn
      for the input. Target is fixed because at most one dimension could be dyn.
      We have different cases for this.
  
      Args:
          list_of_dims: The input list of dimensions
          target: The tensor we want to reshape to
  
      Returns: A disjunction of transformed reshape constraints
  
      """
      all_possibilities = generate_all_int_dyn_dim_possibilities(
          list_of_dims
      )
  
      all_constraints = []
  
      for p in all_possibilities:
          to_multiply = []
  
          p = list(p)
  
          for constraint in p:
              assert isinstance(constraint, BinConstraintD)
              if constraint.op == op_neq:
                  to_multiply.append(constraint.lhs)
  
          if not to_multiply:
              all_constraints.append(Conj(p))
  
          elif len(to_multiply) < len(list_of_dims):
              all_constraints.append(
                  Conj(
                      p
                      + [
                          is_target_div_by_dim(
                              target, Prod(to_multiply)
                          )
                      ]
                  )
              )
          else:
              all_constraints.append(
                  Conj(
                      p
                      + [
                          BinConstraintD(
                              Prod(list_of_dims), Prod(target), op_eq
                          )
                      ]
                  )
              )
  
      return Disj(all_constraints)
  
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 24 for calling a global function with example usage: gen_all_reshape_possibilities(list_of_dims, target) and returns: Disj