code: def forward(self, category, input, hidden):
      input_combined = torch.cat((category, input, hidden), 1)
      hidden = self.i2h(input_combined)
      output = self.i2o(input_combined)
      output_combined = torch.cat((hidden, output), 1)
      output = self.o2o(output_combined)
      output = self.dropout(output)
      output = self.softmax(output)
      return output, hidden, input
  
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 2 for calling the RNN obj's underlying member method code with example usage: obj.forward(category, input, hidden) and returns: output,  hidden,  input