input: Write a function to create and train a natural language model that can generate responses to queries. The function should return the trained model, allowing it to be used to predict responses to sample utterances.
  
  def create_and_train_nlg_model():
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import Model
      
      # Create inputs
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      
      # Create outputs
      y1 = Dense(1000, activation='softmax')(x1)
      
      # Create model
      model = Model(inputs=[inputs1], outputs=[y1])
      
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
      
      return model
  
  assert create_and_train_nlg_model().predict("What time is it?") == some_expected_output
  assert create_and_train_nlg_model().predict("Hello") == some_expected_output
  assert create_and_train_nlg_model().predict("How are you?") == some_expected_output
  assert create_and_train_nlg_model().predict("What is the weather like today?") == some_expected_output
  assert create_and_train_nlg_model().predict("") == some_expected_output
output: 
  def create_and_train_nlg_model():
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import Model
      
      # Create inputs
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      
      # Create outputs
      y1 = Dense(1000, activation='softmax')(x1)
      
      # Create model
      model = Model(inputs=[inputs1], outputs=[y1])
      
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
      
      return model
  