i use modal can you patch me my code :
import time
import modal
from modal import App, Image, NetworkFileSystem, wsgi_app

dockerhub_image = Image.from_registry(
    "tensorflow/tensorflow:2.12.0-gpu",
).pip_install("protobuf==3.20.*","datasets","tensorflow_text")

app = App(
    "example-tensorflow-tutorial", image=dockerhub_image
)  # Note: prior to April 2024, "app" was called "stub"

@app.function()

def download():
    import datasets
    from datasets import load_dataset
    import tensorflow as tf
    dataset = load_dataset("Helsinki-NLP/opus-100", "en-fr")
    data = dataset["train"]
    val = dataset["validation"]

    french_sentences = [example["fr"] for example in data["translation"]]
    english_sentences = [example["en"] for example in data["translation"]]
    vfrench_sentences = [example["fr"] for example in val["translation"]]
    venglish_sentences = [example["en"] for example in val["translation"]]
    dataset = tf.data.Dataset.from_tensor_slices((french_sentences, english_sentences))
    vdataset = tf.data.Dataset.from_tensor_slices((vfrench_sentences, venglish_sentences))

    french_sentences_decoded = []
    english_sentences_decoded = []

    vfrench_sentences_decoded = []
    venglish_sentences_decoded = []

    for french_sentence, english_sentence in dataset.take(len(french_sentences)):
        french_sentences_decoded.append("b '"+french_sentence.numpy().decode('utf-8'))
        english_sentences_decoded.append("b '"+english_sentence.numpy().decode('utf-8'))


    for french_sentence, english_sentence in dataset.take(len(vfrench_sentences)):
        vfrench_sentences_decoded.append("b '"+french_sentence.numpy().decode('utf-8'))
        venglish_sentences_decoded.append("b '"+english_sentence.numpy().decode('utf-8'))


    print("Nombre de phrases en français :", len(french_sentences_decoded))
    print("Nombre de phrases en anglais :", len(english_sentences_decoded))
    print("Nombre de phrases en français pour la validation :", len(vfrench_sentences_decoded))
    print("Nombre de phrases en anglais pour la validation :", len(venglish_sentences_decoded))
    return french_sentences_decoded, english_sentences_decoded,vfrench_sentences_decoded,venglish_sentences_decoded
def double(french_sentences,english_sentences):
    train_fr = french_sentences
    train_en = english_sentences
    return train_en, train_fr
def vocab(french_sentences,english_sentences):
    from tensorflow_text.tools.wordpiece_vocab import bert_vocab_from_dataset as bert_vocab

    bert_tokenizer_params = dict(lower_case=False)
    reserved_tokens = ["[START]","[END]","[PAD]","[UNK]"]

    bert_vocab_args = {
        'vocab_size': 8000,
        'reserved_tokens': reserved_tokens,
        'bert_tokenizer_params': bert_tokenizer_params,
        'learn_params': {},
    }

    from tensorflow_text.tools.wordpiece_vocab import bert_vocab_from_dataset as bert_vocab
    en_vocab = bert_vocab.bert_vocab_from_dataset(
        tf.data.Dataset.from_tensor_slices(english_sentences).batch(64).prefetch(2),
        **bert_vocab_args
    )

    fr_vocab = bert_vocab.bert_vocab_from_dataset(
            tf.data.Dataset.from_tensor_slices(french_sentences).batch(64).prefetch(2),
            **bert_vocab_args
        )
    return fr_vocab,en_vocab
def write_vocab_file(filepath, vocab):
    with open(filepath, 'w', encoding='utf-8') as f:
        for token in vocab:
            print(token, file=f)
def tokenizer():
    fr_tokenizer = tf_text.BertTokenizer('fr_vocab.txt', **bert_tokenizer_params)
    en_tokenizer = tf_text.BertTokenizer('en_vocab.txt', **bert_tokenizer_params)
def cmodel(en_vocab,fr_vocab,reserved_tokens):
    max_length = 200
    inputs = tf.keras.Input(shape=(1,200))
    lstm_units = 512
    vocab_size_en = len(en_vocab) + len(reserved_tokens)
    vocab_size_fr = len(fr_vocab) + len(reserved_tokens)
    embedding_dim = 512
    print(vocab_size_en,vocab_size_fr)

    encoder_inputs = tf.keras.layers.Input(shape=(200,))
    encoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size_fr, output_dim=embedding_dim, mask_zero=True)(encoder_inputs)
    encoder_outputs, state_h, state_c = tf.keras.layers.LSTM(lstm_units, return_state=True)(encoder_embedding)
    encoder_states = [state_h, state_c]

    decoder_inputs = tf.keras.layers.Input(shape=(200,))
    decoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size_fr, output_dim=embedding_dim, mask_zero=True)(decoder_inputs)
    decoder_lstm = tf.keras.layers.LSTM(lstm_units, return_sequences=True, return_state=True)
    decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)
    decoder_dense = tf.keras.layers.Dense(vocab_size_en, activation='Softmax')
    decoder_outputs = decoder_dense(decoder_outputs)
    model = tf.keras.models.Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_outputs)
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])


    model.summary()
def format_sequences(tokenizer, sentences, max_length):
    sequences = tokenizer.tokenize(sentences).merge_dims(-2, -1)
    padded_sequences = sequences.to_tensor(default_value=0, shape=[None, max_length])
    return padded_sequences
def formattime(fr_tokenizer,train_fr,train_en,max_length,en_tokenizer):
    encoder_input_data = format_sequences(fr_tokenizer, train_fr, max_length)
    decoder_input_data = format_sequences(fr_tokenizer, ["[START]" + sentence for sentence in train_fr], max_length)
    decoder_target_data = format_sequences(en_tokenizer, [sentence +"[END]" for sentence in train_en], max_length)
def training(model,encoder_input_data, decoder_input_data,decoder_target_data):
    from tensorflow.keras.utils import  plot_model
    from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

    batch_size = 64
    epochs = 33

    model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
            batch_size=batch_size,
            epochs=epochs,
            validation_split=0.2)

    model.save_weights('CACATOR.h5')          
    print("le modele a été fait ")

@app.local_entrypoint()

def main():
    LIBRARY()
    print(download.remote())
    double.remote((french_sentences,english_sentences))
    vocab.remote((train_fr,train_en,french_sentences,english_sentences))
    write_vocab_file.remote(('fr_vocab.txt', fr_vocab))
    write_vocab_file.remote(('en_vocab.txt', en_vocab))
    tokenizer.remote()
    cmodel.remote(en_vocab,fr_vocab,reserved_tokens)
    formattime.remote(fr_tokenizer,train_fr,train_en,max_length,en_tokenizer)
    training.remote(model,encoder_input_data, decoder_input_data,decoder_target_data)