text: """Enables dynamic setting of underlying Keras module.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  _KERAS_BACKEND = None
  _KERAS_LAYERS = None
  _KERAS_MODELS = None
  _KERAS_UTILS = None
  
  
  def get_submodules_from_kwargs(kwargs):
      backend = kwargs.get('backend', _KERAS_BACKEND)
      layers = kwargs.get('layers', _KERAS_LAYERS)
      models = kwargs.get('models', _KERAS_MODELS)
      utils = kwargs.get('utils', _KERAS_UTILS)
      for key in kwargs.keys():
          if key not in ['backend', 'layers', 'models', 'utils']:
              raise TypeError('Invalid keyword argument: %s', key)
      return backend, layers, models, utils
  
  
  def correct_pad(backend, inputs, kernel_size):
      """Returns a tuple for zero-padding for 2D convolution with downsampling.
  
      # Arguments
          input_size: An integer or tuple/list of 2 integers.
          kernel_size: An integer or tuple/list of 2 integers.
  
      # Returns
          A tuple.
      """
      img_dim = 2 if backend.image_data_format() == 'channels_first' else 1
      input_size = backend.int_shape(inputs)[img_dim:(img_dim + 2)]
  
      if isinstance(kernel_size, int):
          kernel_size = (kernel_size, kernel_size)
  
      if input_size[0] is None:
          adjust = (1, 1)
      else:
          adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)
  
      correct = (kernel_size[0] // 2, kernel_size[1] // 2)
  
      return ((correct[0] - adjust[0], correct[0]),
              (correct[1] - adjust[1], correct[1]))
  