input: Write a python function to create a sentiment analysis model using the given code.
  assert isinstance(sentiment_model("I love this!"), np.ndarray)
  assert isinstance(sentiment_model("This is terrible."), np.ndarray)
  assert len(sentiment_model("Not bad, could be better.")) == 1
output: def sentiment_model(text):
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Embedding, LSTM, Dense
      import numpy as np
  
      # Example training data
      texts = ["I love this!", "This is terrible.", "Not bad, could be better."]
      labels = [1, 0, 0.5]  # Labels should be consistent with your actual training data
  
      # Tokenize the text
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(texts)
      sequences = tokenizer.texts_to_sequences(texts)
      word_index = tokenizer.word_index
      data = pad_sequences(sequences, maxlen=10)
  
      # Build a simple model
      model = Sequential()
      model.add(Embedding(input_dim=len(word_index) + 1, output_dim=32, input_length=10))
      model.add(LSTM(32))
      model.add(Dense(1, activation='sigmoid'))
      model.compile(optimizer='adam', loss='binary_crossentropy')
  
      # Train the model (This is just a placeholder, you should use real training data)
      model.fit(data, np.array(labels), epochs=1, verbose=0)
  
      # Predict sentiment
      input_seq = tokenizer.texts_to_sequences([text])
      input_data = pad_sequences(input_seq, maxlen=10)
      prediction = model.predict(input_data)
  
      return prediction