code: def set_buffer(
      self,
      size,
      value0=1.0,
      value1=1.0,
      dtype=cp.float32,
      tensor_type0="cupy",
      tensor_type1="cupy",
  ):
      if tensor_type0 == "cupy":
          with cp.cuda.Device(0):
              self.buffer0 = cp.ones(size, dtype=dtype) * value0
      elif tensor_type0 == "torch":
          self.buffer0 = (
              torch.ones(size, dtype=torch.float32).cuda(0) * value0
          )
      else:
          raise RuntimeError()
  
      if tensor_type1 == "cupy":
          with cp.cuda.Device(1):
              self.buffer1 = cp.ones(size, dtype=dtype) * value1
      elif tensor_type1 == "torch":
          self.buffer1 = (
              torch.ones(size, dtype=torch.float32).cuda(1) * value1
          )
      else:
          raise RuntimeError()
      cp.cuda.Device(0).synchronize()
      cp.cuda.Device(1).synchronize()
      # cp.cuda.Stream.null.synchronize()
      return True
  
imports: import cupy
  import logging
  import ray
  import ray.util.collective
  from ray.util.collective.types import Backend, ReduceOp
  from ray.util.collective.collective_group.nccl_util import get_num_gpus
  import torch
label_desc: Use image node_id 5 for calling the MultiGPUWorker obj's underlying member method code with example usage: obj.set_buffer(size, value0, value1, dtype, tensor_type0, tensor_type1) and returns: True