code: def generate_reshape(constraint, counter):
      """
      Transform reshape constraints
      """
      d, counter = gen_tensor_dims(4, counter)
  
      d1 = d[0]
      d2 = d[1]
      d3 = d[2]
      d4 = d[3]
  
      target = constraint.target.__args__
  
      is_fully_static = all(d != Dyn for d in target)
  
      # dynamic tensor
      c1_dyn = BinConstraintT(constraint.src, Dyn, op_eq)
      c2_tensor1 = BinConstraintT(
          constraint.src, TensorType([d1]), op_eq
      )
      c2_tensor2 = BinConstraintT(
          constraint.src, TensorType([d1, d2]), op_eq
      )
      c2_tensor3 = BinConstraintT(
          constraint.src, TensorType([d1, d2, d3]), op_eq
      )
      c2_tensor4 = BinConstraintT(
          constraint.src, TensorType([d1, d2, d3, d4]), op_eq
      )
  
      d1_eq_dyn = BinConstraintD(d1, Dyn, op_eq)
      d1_neq_dyn = BinConstraintD(d1, Dyn, op_neq)
  
      d2_eq_dyn = BinConstraintD(d2, Dyn, op_eq)
      d2_neq_dyn = BinConstraintD(d2, Dyn, op_neq)
  
      d3_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
      d3_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
  
      d4_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
      d4_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
  
      nat_d1 = BinConstraintD(0, d1, op_leq)
      nat_d2 = BinConstraintD(0, d2, op_leq)
      nat_d3 = BinConstraintD(0, d3, op_leq)
      nat_d4 = BinConstraintD(0, d4, op_leq)
  
      if is_fully_static:
          # size 1 tensor
          c3_tensor1 = Disj(
              [
                  d1_eq_dyn,
                  (
                      Conj(
                          [
                              d1_neq_dyn,
                              BinConstraintD(d1, Prod(target), op_eq),
                          ]
                      )
                  ),
              ]
          )
          all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
  
          # size 2 tensor
          all_tensor_2 = Conj(
              [
                  c2_tensor2,
                  gen_all_reshape_possibilities([d1, d2], target),
              ]
          )
  
          # size 3 tensor
          all_tensor_3 = Conj(
              [
                  c2_tensor3,
                  gen_all_reshape_possibilities([d1, d2, d3], target),
              ]
          )
  
          # size 4 tensor
          all_tensor_4 = Conj(
              [
                  c2_tensor4,
                  gen_all_reshape_possibilities(
                      [d1, d2, d3, d4], target
                  ),
              ]
          )
  
          return (
              Conj(
                  [
                      Disj(
                          [
                              c1_dyn,
                              all_tensor_1,
                              all_tensor_2,
                              all_tensor_3,
                              all_tensor_4,
                          ]
                      ),
                      nat_d1,
                      nat_d2,
                      nat_d3,
                      nat_d4,
                  ]
              ),
              counter,
          )
  
      # then there must be exactly one occurrence of dyn
      else:
          new_target = []
  
          for n in target:
              if n != Dyn:
                  new_target.append(n)
  
          # tensor 1
          c3_tensor1 = Disj(
              [
                  d1_eq_dyn,
                  (
                      Conj(
                          [
                              d1_neq_dyn,
                              is_dim_div_by_target(new_target, d1),
                          ]
                      )
                  ),
              ]
          )
          all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
  
          # tensor 2
          c21 = Disj([d1_eq_dyn, d2_eq_dyn])
          c22 = Conj(
              [
                  d1_neq_dyn,
                  d2_neq_dyn,
                  is_dim_div_by_target(new_target, Prod([d1, d2])),
              ]
          )
          all_tensor_2 = Conj([c2_tensor2, Disj([c21, c22])])
  
          # tensor 3
          c31 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn])
          c32 = Conj(
              [
                  d1_neq_dyn,
                  d2_neq_dyn,
                  d3_neq_dyn,
                  is_dim_div_by_target(new_target, Prod([d1, d2, d3])),
              ]
          )
          all_tensor_3 = Conj([c2_tensor3, Disj([c31, c32])])
  
          # tensor 4
          c41 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn, d4_eq_dyn])
          c42 = Conj(
              [
                  d1_neq_dyn,
                  d2_neq_dyn,
                  d3_neq_dyn,
                  d4_neq_dyn,
                  is_dim_div_by_target(
                      new_target, Prod([d1, d2, d3, d4])
                  ),
              ]
          )
          all_tensor_4 = Conj([c2_tensor4, Disj([c41, c42])])
  
          return (
              Conj(
                  [
                      Disj(
                          [
                              c1_dyn,
                              all_tensor_1,
                              all_tensor_2,
                              all_tensor_3,
                              all_tensor_4,
                          ]
                      ),
                      nat_d1,
                      nat_d2,
                      nat_d3,
                      nat_d4,
                  ]
              ),
              counter,
          )
  
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 17 for calling a global function with example usage: generate_reshape(constraint, counter) and returns: Conj,  counter, Conj,  counter