class_docstr: Scheduler for CUDA Kernels, which delegates calls as appropriate
  to the CUDA-C++ and Triton Schedulers, which both work for CUDA devices
  and use a unified-wrapper for codegen.
  
  If Scheduling code needs to be specialized for the case of mixed Triton / CUDA C++ code,
  this would also be the place to do it.
class_docstr_tok: ["Scheduler","for","CUDA","Kernels",",","which","delegates","calls","as","appropriate","to","the","CUDA-C++","and","Triton","Schedulers",",","which","both","work","for","CUDA","devices","and","use","a","unified-wrapper","for","codegen",".","If","Scheduling","code","needs","to","be","specialized","for","the","case","of","mixed","Triton","\/","CUDA","C++","code",",","this","would","also","be","the","place","to","do","it","."]
code: def codegen_foreach(self, *args, **kwargs):
      return self._triton_scheduling.codegen_foreach(*args, **kwargs)
  
file_path: pytorch/torch/_inductor/codegen/cuda_combined_scheduling.py
imports: from typing import List
  from ..scheduler import BaseSchedulerNode, BaseScheduling, Scheduler, SchedulerNode
  from .cuda.cuda_cpp_scheduling import CUDACPPScheduling
  from .triton import TritonScheduling
label_desc: Use image node_id 10 for calling the CUDACombinedScheduling obj's underlying member method code with example usage: obj.codegen_foreach() and returns: self