code: def trainbench(
      name,
      rnn_creator,
      nloops=100,
      warmup=10,
      seqLength=100,
      numLayers=1,
      inputSize=512,
      hiddenSize=512,
      miniBatch=64,
      device="cuda",
      seed=None,
  ):
      def train_batch(modeldef):
          # CUDA events for timing
          if device == "cuda":
              timer_class = torch.cuda.Event
          else:
              timer_class = Event
  
          fwd_start_event = timer_class(enable_timing=True)
          fwd_end_event = timer_class(enable_timing=True)
          bwd_start_event = timer_class(enable_timing=True)
          bwd_end_event = timer_class(enable_timing=True)
  
          gc.collect()
  
          fwd_start_event.record()
          with record_function("## forward ##"):
              forward_output = modeldef.forward(*modeldef.inputs)
          fwd_end_event.record()
  
          # XXX: Use if need to print something
          # print(modeldef.forward.graph_for(*modeldef.inputs))
  
          if modeldef.backward_setup is not None:
              backward_input = modeldef.backward_setup(forward_output)
          else:
              backward_input = forward_output
  
          gc.collect()
  
          bwd_start_event.record()
          if modeldef.backward is not None:
              modeldef.backward(*backward_input)
          bwd_end_event.record()
  
          if modeldef.backward is not None:
              with torch.no_grad():
                  for param in modeldef.params:
                      assert param.grad is not None
                      param.grad.zero_()
  
          if device == "cuda":
              torch.cuda.synchronize()
  
          fwd_time = fwd_start_event.elapsed_time(fwd_end_event)
          bwd_time = bwd_start_event.elapsed_time(bwd_end_event)
          return fwd_time, bwd_time
  
      creator_args = creator_args = {
          "seqLength": seqLength,
          "numLayers": numLayers,
          "inputSize": inputSize,
          "hiddenSize": hiddenSize,
          "miniBatch": miniBatch,
          "device": device,
          "seed": seed,
      }
  
      modeldef = rnn_creator(**creator_args)
  
      [train_batch(modeldef) for _ in range(warmup)]
  
      results = [train_batch(modeldef) for _ in range(nloops)]
      fwd_times, bwd_times = zip(*results)
  
      fwd_times = torch.tensor(fwd_times)
      bwd_times = torch.tensor(bwd_times)
      return BenchResult(
          name=name,
          avg_fwd=fwd_times.mean().item(),
          std_fwd=fwd_times.std().item(),
          info_fwd=fwd_times,
          avg_bwd=bwd_times.mean().item(),
          std_bwd=bwd_times.std().item(),
          info_bwd=bwd_times,
      )
  
imports: import argparse
  import copy
  import gc
  import json
  import sys
  import time
  from collections import namedtuple
  import torch
  from torch.autograd.profiler import record_function
  from .fuser import set_fuser
  from .runner import get_nn_runners
label_desc: Use image node_id 5 for calling a global function with example usage: trainbench(name, rnn_creator, nloops, warmup, seqLength, numLayers, inputSize, hiddenSize, miniBatch, device, seed) and returns: BenchResult, fwd_time,  bwd_time