code: def generate_broadcasting(constraint, counter):
      """
      Transform broadcasting constraints
      """
      e11, e12 = constraint.res1, constraint.res2
      e1, e2 = constraint.input1, constraint.input2
  
      e1_dyn = BinConstraintT(e1, Dyn, op_eq)
      e2_dyn = BinConstraintT(e2, Dyn, op_eq)
  
      # Introduce dimensions
      e1_equal_e11 = BinConstraintT(e1, e11, op_eq)
      e2_equal_e12 = BinConstraintT(e2, e12, op_eq)
  
      # dyn possibility
      e1_dyn_constraint = Conj([e1_dyn, e1_equal_e11, e2_equal_e12])
      e2_dyn_constraint = Conj([e2_dyn, e1_equal_e11, e2_equal_e12])
  
      # tensor possibility
      # generate dimensions to create tensors of size 1
      (
          final_tensor_1_constraint,
          _,
          _,
          nat_dims_1,
          counter,
      ) = gen_broadcasting_constraints(e1, e2, e11, e12, 1, counter)
  
      # generate dimensions to create tensors of size 2
      (
          final_tensor_2_constraint_no_padding,
          final_tensor_2_constraint_padding_arg1,
          final_tensor_2_constraint_padding_arg2,
          nat_dims_2,
          counter,
      ) = gen_broadcasting_constraints(e1, e2, e11, e12, 2, counter)
  
      # generate dimensions to create tensors of size 3
      (
          final_tensor_3_constraint_no_padding,
          final_tensor_3_constraint_padding_arg1,
          final_tensor_3_constraint_padding_arg2,
          nat_dims_3,
          counter,
      ) = gen_broadcasting_constraints(e1, e2, e11, e12, 3, counter)
  
      # generate dimensions to create tensors of size 4
      (
          final_tensor_4_constraint_no_padding,
          final_tensor_4_constraint_padding_arg1,
          final_tensor_4_constraint_padding_arg2,
          nat_dims_4,
          counter,
      ) = gen_broadcasting_constraints(e1, e2, e11, e12, 4, counter)
  
      final_result = Disj(
          [
              e1_dyn_constraint,
              e2_dyn_constraint,
              final_tensor_1_constraint,
              final_tensor_2_constraint_no_padding,
              final_tensor_2_constraint_padding_arg1,
              final_tensor_2_constraint_padding_arg2,
              final_tensor_3_constraint_no_padding,
              final_tensor_3_constraint_padding_arg1,
              final_tensor_3_constraint_padding_arg2,
              final_tensor_4_constraint_no_padding,
              final_tensor_4_constraint_padding_arg1,
              final_tensor_4_constraint_padding_arg2,
          ]
      )
  
      return (
          Conj(
              [
                  final_result,
                  *nat_dims_1,
                  *nat_dims_2,
                  *nat_dims_3,
                  *nat_dims_4,
              ]
          ),
          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 18 for calling a global function with example usage: generate_broadcasting(constraint, counter) and returns: Conj,  counter