class_docstr: Non-cuDNN LSTM that supports double backprop.
  
  This is a workaround to address the issue that cuDNN RNNs in PyTorch
  do not support double backprop.
  
  See https://github.com/pytorch/pytorch/issues/5261.
class_docstr_tok: ["Non-cuDNN","LSTM","that","supports","double","backprop",".","This","is","a","workaround","to","address","the","issue","that","cuDNN","RNNs","in","PyTorch","do","not","support","double","backprop",".","See","https",":","\/\/github.com\/pytorch\/pytorch\/issues\/5261","."]
code: def forward(self, x, recurrent_state):
      with torch.backends.cudnn.flags(enabled=False):
          return super().forward(x, recurrent_state)
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import trpo
  from pfrl.envs.abc import ABC
  from pfrl.experiments import train_agent_batch_with_evaluation, train_agent_with_evaluation
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes, run_evaluation_episodes
  from pfrl.nn import RecurrentSequential
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 1 for calling the NonCudnnLSTM obj's underlying member method code with example usage: obj.forward(x, recurrent_state) and returns: super