InvalidArgumentError: indices[120,0] = 3080 is not in [0, 32) [[{{node embedding_6/embedding_lookup}}]]

I have seen others have posted similar questions. But the difference is I'm running a Keras Functional API instead of a sequential model.

from keras.models import Model
from keras import layers
from keras import Input
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500

text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input)
encoded_text = layers.LSTM(32)(embedded_text)
question_input = Input(shape=(None,), dtype='int32', name='question')
embedded_question = layers.Embedding( 32, question_vocabulary_size)(question_input)
encoded_question = layers.LSTM(16)(embedded_question)

concatenated = layers.concatenate([encoded_text, encoded_question],axis=-1) 

## Concatenates the encoded question and encoded text

answer = layers.Dense(answer_vocabulary_size, activation='softmax')(concatenated)
model = Model([text_input, question_input], answer)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])

Feeding data to a multi-input model

import numpy as np
num_samples = 1000
max_length = 100

text = np.random.randint(1, text_vocabulary_size, size=(num_samples, max_length))

question = np.random.randint(1, question_vocabulary_size,  size=(num_samples, max_length))

answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size)) 

Fitting using a list of inputs

model.fit([text, question], answers, epochs=10, batch_size=128)

The error I get while trying into fitting the model is as follows.

InvalidArgumentError: indices[120,0] = 3080 is not in [0, 32)
     [[{{node embedding_6/embedding_lookup}}]]

The dimension of the vocabulary is the first parameter to the Embedding class, you are setting them as the second. You just have to switch the parameters you are giving to the embedding instances.