code: def generate_binconstraint_d(constraint, counter):
      """
      Transform binary constraints for dimensions
      """
      if constraint.op == op_precision:
          if isinstance(constraint.lhs, int):
              return (
                  BinConstraintD(constraint.lhs, constraint.rhs, op_eq),
                  counter,
              )
          elif constraint.lhs == Dyn:
              return T(), counter
  
      elif constraint.op == op_consistency:
          return (
              Disj(
                  [
                      BinConstraintD(
                          constraint.lhs, constraint.rhs, op_eq
                      ),
                      BinConstraintD(constraint.rhs, Dyn, op_eq),
                      BinConstraintD(constraint.lhs, Dyn, op_eq),
                  ]
              ),
              counter,
          )
  
      else:
          return 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 9 for calling a global function with example usage: generate_binconstraint_d(constraint, counter) and returns: BinConstraintD,  counter, Disj,  counter, constraint,  counter, T,  counter