code: def __init__(self, input_size, hidden_size, output_size):
      super(RNN, self).__init__()
      self.hidden_size = hidden_size
      self.i2h = nn.Linear(
          n_categories + input_size + hidden_size, hidden_size
      )
      self.i2o = nn.Linear(
          n_categories + input_size + hidden_size, output_size
      )
      self.o2o = nn.Linear(hidden_size + output_size, output_size)
      self.dropout = nn.Dropout(0.1)
      self.softmax = nn.LogSoftmax(dim=1)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from torch.autograd import Variable
  from tensorboardX import SummaryWriter
  import pytest
label_desc: Use image node_id 1 to create a new RNN object from inherited base classes: nn with example: obj = RNN(input_size, hidden_size, output_size)