code: def broadcast_dim(
      tensor_input1, tensor_input2, res1, res2, index, padding=False
  ):
      """
      Apply broadcasting to the 'index' dimension of tensor_input1.
      Args:
          tensor_input1: should represent [d1, ..., d_index, ...] where d_index = 1
          tensor_input2: represents the second input
          res1: broadcasted result 1
          res2: broadcasted result 2
          index: the index to broadcast
          padding: If padding was used, then tensor_input1[index] does not exist
  
      Returns:
  
      """
      if tensor_input1[index] is None:
          assert padding
  
      if not padding:
          # then the inputs are the same length so they all have dimensions at "index"
          return Conj(
              [
                  BinConstraintD(tensor_input1[index], 1, op_eq),
                  BinConstraintD(res1[index], res2[index], op_eq),
                  BinConstraintD(
                      res2[index], tensor_input2[index], op_eq
                  ),
              ]
          )
  
      else:
          # we don't set the input dimension to 1, since it doesn't exist.
          return Conj(
              [
                  BinConstraintD(res1[index], res2[index], op_eq),
                  BinConstraintD(
                      res2[index], tensor_input2[index], op_eq
                  ),
              ]
          )
  
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 25 for calling a global function with example usage: broadcast_dim(tensor_input1, tensor_input2, res1, res2, index, padding) and returns: Conj, Conj