code: def transform_transpose(constraint, counter):
      """
      Similar to a sequence of two index-selects
      """
      dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
      is_valid_index1 = valid_index(constraint.index1, dims)
      is_valid_index2 = valid_index(constraint.index2, dims)
      new_dims = copy.deepcopy(dims)
      nat_constraints = gen_nat_constraints(dims)
  
      if is_valid_index1 == T() and is_valid_index2 == T():
          new_dims[constraint.index1] = dims[constraint.index2]
          new_dims[constraint.index2] = dims[constraint.index1]
  
      transformed_constraint = Conj(
          [
              BinConstraintT(
                  constraint.input_var, TensorType(dims), op_eq
              ),
              *nat_constraints,
              is_valid_index1,
              is_valid_index2,
              BinConstraintT(
                  constraint.output, TensorType(new_dims), op_eq
              ),
          ]
      )
      return transformed_constraint, 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 3 for calling a global function with example usage: transform_transpose(constraint, counter) and returns: transformed_constraint,  counter