code: def train(
      input_tensor,
      target_tensor,
      encoder,
      decoder,
      encoder_optimizer,
      decoder_optimizer,
      criterion,
      max_length=MAX_LENGTH,
      device=default_device,
  ):
      encoder_hidden = encoder.initHidden()
  
      encoder_optimizer.zero_grad()
      decoder_optimizer.zero_grad()
  
      input_length = input_tensor.size(0)
      target_length = target_tensor.size(0)
  
      encoder_outputs = torch.zeros(
          max_length, encoder.hidden_size, device=device
      )
  
      loss = 0
  
      with LightwoodAutocast():
          for ei in range(min(input_length, len(encoder_outputs))):
              encoder_output, encoder_hidden = encoder(
                  input_tensor[ei], encoder_hidden
              )
              encoder_outputs[ei] = encoder_output[0, 0]
  
          decoder_input = torch.tensor([[SOS_token]], device=device)
  
          decoder_hidden = encoder_hidden
  
          use_teacher_forcing = (
              True if random.random() < teacher_forcing_ratio else False
          )
  
          if use_teacher_forcing:
              # Teacher forcing: Feed the target as the next input
              for di in range(target_length):
                  if isinstance(decoder, AttnDecoderRNN):
                      (
                          decoder_output,
                          decoder_hidden,
                          decoder_attention,
                      ) = decoder(
                          decoder_input, decoder_hidden, encoder_outputs
                      )
                  else:
                      decoder_output, decoder_hidden = decoder(
                          decoder_input, decoder_hidden
                      )
                  loss += criterion(decoder_output, target_tensor[di])
                  decoder_input = target_tensor[di]  # Teacher forcing
  
          else:
              # Without teacher forcing: use its own predictions as the next input
              for di in range(target_length):
                  if isinstance(decoder, AttnDecoderRNN):
                      (
                          decoder_output,
                          decoder_hidden,
                          decoder_attention,
                      ) = decoder(
                          decoder_input, decoder_hidden, encoder_outputs
                      )
                  else:
                      decoder_output, decoder_hidden = decoder(
                          decoder_input, decoder_hidden
                      )
                  topv, topi = decoder_output.topk(1)
                  decoder_input = (
                      topi.squeeze().detach()
                  )  # detach from history as input
  
                  loss += criterion(decoder_output, target_tensor[di])
                  if decoder_input.item() == EOS_token:
                      break
  
      loss.backward()
  
      encoder_optimizer.step()
      decoder_optimizer.step()
  
      return loss.item() / target_length
  
imports: from __future__ import unicode_literals, print_function, division
  import math
  import time
  from io import open
  import unicodedata
  import string
  import re
  import random
  import operator
  import torch
  import torch.nn
  from torch import optim
  import torch.nn.functional
  from lightwood.helpers.torch import LightwoodAutocast
  from lightwood.helpers.log import log
label_desc: Use image node_id 7 for calling a global function with example usage: train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length, device) and returns: unknown