input: Write a python function to tokenize, pad sequences, and predict sentiment using a pre-trained Keras model on a given sentence.
  assert predict_sentiment("It is a wonderful day") == 1
  assert predict_sentiment("the weather is horrible") == 0
  assert predict_sentiment("today is a great day") == 1
output: def predict_sentiment(sentence):
      from keras.preprocessing.text import Tokenizer
      from keras.preprocessing.sequence import pad_sequences
      from keras.models import load_model
      import numpy as np
  
      # Load pre-trained Keras model
      model = load_model('path_to_your_model.h5')
  
      # Tokenize the sentence
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts([sentence])
      sequence = tokenizer.texts_to_sequences([sentence])
  
      # Pad the sequence
      padded_sequence = pad_sequences(sequence, maxlen=100)  # Adjust 'maxlen' as per your model's requirement
  
      # Predict sentiment
      prediction = model.predict(padded_sequence)
      
      # Assuming a binary classification with 0 as negative and 1 as positive sentiment
      sentiment = int(np.round(prediction[0][0]))
  
      return sentiment