hidden_size = 128
batch_size = 32

input_lang, output_lang, [train_dataloader](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader") = get_dataloader(batch_size)

encoder = [EncoderRNN](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")(input_lang.n_words, hidden_size).to([device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device "torch.device"))
decoder = [AttnDecoderRNN](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")(hidden_size, output_lang.n_words).to([device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device "torch.device"))

train([train_dataloader](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader"), encoder, decoder, 80, print_every=5, plot_every=5)