input: Write a function to generate natural language responses using a TensorFlow-based model. The function should compile and fit the model with given inputs and outputs, and then predict the response to a sample utterance. The function should return the prediction.
  assert generate_response("What time is it?")==[expected_output]
  assert generate_response("How are you?")==[expected_output]
  assert generate_response("Tell me a joke.")==[expected_output]
output: def generate_response(utterance):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      import numpy as np
  
      # Example data
      inputs = np.array([["What time is it?"], ["How are you?"], ["Tell me a joke."]])
      outputs = np.array([[0], [1], [2]])
  
      # Model creation
      model = Sequential([
          Dense(64, activation='relu', input_shape=(inputs.shape[1],)),
          Dense(64, activation='relu'),
          Dense(outputs.shape[1], activation='softmax')
      ])
  
      # Model compilation
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Model fitting
      model.fit(inputs, outputs, epochs=10)
  
      # Prediction
      prediction = model.predict(np.array([[utterance]]))
      return prediction