ValueError: Shapes (None, 20, 9) and (None, 9) are incompatible

This is my code

import tensorflow as tf
from tensorflow import keras
from keras import layers
# definition  question and answer
questions = [
    "你好嗎？",
    "今天天氣如何？",
    "你有什麼興趣？",
    "你喜歡什麼食物？"
]

answers = [
    "我很好，謝謝你。",
    "今天天氣非常好。",
    "我喜歡看書和打電子遊戲。",
    "我喜歡吃中式食物，特別是炒飯。"
]
# 將問題和回答轉換成數字
tokenizer = keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(questions + answers)


question_seqs = tokenizer.texts_to_sequences(questions)
answer_seqs = tokenizer.texts_to_sequences(answers)

# 將問題和回答填充到相同的長度
max_len = 20

question_seqs_padded = keras.preprocessing.sequence.pad_sequences(question_seqs, maxlen=max_len)
answer_seqs_padded = keras.preprocessing.sequence.pad_sequences(answer_seqs, maxlen=max_len)

# 定義模型
model = keras.Sequential()
model.add(layers.Embedding(len(tokenizer.word_index) + 1, 50, input_length=max_len))
model.add(layers.LSTM(64))
model.add(layers.Dense(len(tokenizer.word_index) + 1, activation='softmax'))
# 編譯模型
model.compile(loss='categorical_crossentropy', optimizer='adam')


# 訓練模型
model.fit(question_seqs_padded, keras.utils.to_categorical(answer_seqs_padded, num_classes=len(tokenizer.word_index)+1), epochs=100, batch_size=32)

and this is it ran out of error

    ValueError: Shapes (None, 20, 9) and (None, 9) are incompatible

I tryed to fix the Shapes (None, 20, 9) and (None, 9) are incompatible

model.fit(question_seqs_padded, keras.utils.to_categorical(answer_seqs_padded, num_classes=len(tokenizer.word_index)+1), epochs=100, batch_size=32)

I try to delete the answer_seqs_padded to incompatible (None, 9),but it is still not work.

In this case, you need to return the whole sequence in lstm, so just use:
layers.LSTM(64, return_sequences=True) instead. If you don't use return_sequences=True, it will just return the last output.