input: Write a python function to create and compile a TensorFlow model with specific input, hidden, and output layers.
  assert isinstance(create_model().layers[0], tf.keras.layers.InputLayer)
  assert isinstance(create_model().layers[1], tf.keras.layers.Dense)
  assert isinstance(create_model().layers[2], tf.keras.layers.Dense)
  assert isinstance(create_model().layers[3], tf.keras.layers.Dense)
  assert create_model().loss == 'mean_squared_error'
  assert create_model().optimizer._name == 'adam'
output: def create_model():
      import tensorflow as tf
      
      model = tf.keras.Sequential([
          tf.keras.layers.InputLayer(input_shape=(10,)),
          tf.keras.layers.Dense(64, activation='relu'),
          tf.keras.layers.Dense(64, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      return model