code: def forward(self, *args, record_rng=False, set_rng=False, **kwargs):
      if record_rng:
          self.record_rng(*args)
  
      if not set_rng:
          return self.net(*args, **kwargs)
  
      rng_devices = []
      if self.cuda_in_fwd:
          rng_devices = self.gpu_devices
  
      with torch.random.fork_rng(devices=rng_devices, enabled=True):
          torch.set_rng_state(self.cpu_state)
          if self.cuda_in_fwd:
              set_device_states(self.gpu_devices, self.gpu_states)
          return self.net(*args, **kwargs)
  
imports: import torch
  import torch.nn
  from operator import itemgetter
  from torch.autograd.function import Function
  from torch.utils.checkpoint import get_device_states, set_device_states
label_desc: Use image node_id 3 for calling the Deterministic obj's underlying member method code with example usage: obj.forward() and returns: self, self