input: 
  from typing import List, Tuple
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import plot_model
  
  def create_response_model(utterance: str) -> List[Tuple]:
      """Generates a response model for a given natural language utterance. The model 
      is built using Keras layers such as Embedding, GRU, and Dense. The function will 
      compile and train the model, then return the training details as a list of tuples.
      
      Example:
      >>> create_response_model("What time is it?")
      [(10, 32)]
      """
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      y1 = Dense(1000, activation='softmax')(x1)
      model = Model(inputs=[inputs1], outputs=[y1])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit([inputs1], [outputs], epochs=10, batch_size=32)
      train_details = [(10, 32)]
      return train_details
  
output:     inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      y1 = Dense(1000, activation='softmax')(x1)
      model = Model(inputs=[inputs1], outputs=[y1])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit([inputs1], [y1], epochs=10, batch_size=32)
      train_details = [(10, 32)]
      return train_details