class_docstr: RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines
  Uses a separate rng for each sample to reduce memoery usage
class_docstr_tok: ["RNG","to","deterministically","sample","num_samples","samples","that","does","not","depend","on","batch_size","or","mpi_machines","Uses","a","separate","rng","for","each","sample","to","reduce","memoery","usage"]
code: def set_seed(self, seed):
      [
          rng_cpu.manual_seed(i + self.num_samples * seed)
          for i, rng_cpu in enumerate(self.rng_cpu)
      ]
      if th.cuda.is_available():
          [
              rng_cuda.manual_seed(i + self.num_samples * seed)
              for i, rng_cuda in enumerate(self.rng_cuda)
          ]
  
imports: import torch
  import torch.distributed
  from .None import dist_util
label_desc: Use image node_id 9 for calling the DeterministicIndividualGenerator obj's underlying member method code with example usage: obj.set_seed(seed) without return types