<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def main( lang='deu', n=900, epochs=50, batch_size=64, num_neurons=256, encoder_input_data=None, decoder_input_data=None, decoder_target_data=None, checkpoint_dir=os.path.join(BIGDATA_PATH, 'checkpoints'), ):
 """ Train an LSTM encoder-decoder squence-to-sequence model on Anki flashcards for international translation Train on 360 samples, validate on 40 samples Epoch 1/3 8 # 64 common characters in German, 56 in English True 32 """

    mkdir_p(checkpoint_dir)
    encoder_input_path = os.path.join(
        checkpoint_dir,
        'nlpia-ch10-translate-input-{}.npy'.format(lang))
    decoder_input_path = os.path.join(
        checkpoint_dir,
        'nlpia-ch10-translate-decoder-input-{}.npy'.format(lang))
    decoder_target_path = os.path.join(
        checkpoint_dir,
        'nlpia-ch10-translate-target-{}.npy'.format('eng'))
    data_paths = (encoder_input_path, decoder_input_path, decoder_target_path)

    encoder_input_data = []
    if all([os.path.isfile(p) for p in data_paths]):
        encoder_input_data = np.load(encoder_input_path)
        decoder_input_data = np.load(decoder_input_path)
        decoder_target_data = np.load(decoder_target_path)
    if len(encoder_input_data) < n:
        encoder_input_data, decoder_input_data, decoder_target_data = onehot_char_training_data(
            lang=lang, n=n, data_paths=data_paths)
    encoder_input_data = encoder_input_data[:n]
    decoder_input_data = decoder_input_data[:n] 
    decoder_target_data = decoder_target_data[:n]
    model = fit(data_paths=data_paths, epochs=epochs, batch_size=batch_size, num_neurons=num_neurons)
    return model