Pools for an area in features_2d.

  Args:
    features_2d: a Tensor in a shape of [batch_size, height, width, depth].
    area_width: the max width allowed for an area.
    area_height: the max height allowed for an area.
    batch_size: the batch size.
    width: the width of the memory.
    height: the height of the memory.
    depth: the depth of the features.
    fn: the TF function for the pooling.
    name: the op name.
  Returns:
    pool_tensor: A Tensor of shape [batch_size, num_areas, depth]

def _pool_one_shape(features_2d, area_width, area_height, batch_size,
                    width, height, depth, fn=tf.reduce_max, name=None):
  """Pools for an area in features_2d.

  Args:
    features_2d: a Tensor in a shape of [batch_size, height, width, depth].
    area_width: the max width allowed for an area.
    area_height: the max height allowed for an area.
    batch_size: the batch size.
    width: the width of the memory.
    height: the height of the memory.
    depth: the depth of the features.
    fn: the TF function for the pooling.
    name: the op name.
  Returns:
    pool_tensor: A Tensor of shape [batch_size, num_areas, depth]
  """
  with tf.name_scope(name, default_name="pool_one_shape"):
    images = []
    for y_shift in range(area_height):
      image_height = tf.maximum(height - area_height + 1 + y_shift, 0)
      for x_shift in range(area_width):
        image_width = tf.maximum(width - area_width + 1 + x_shift, 0)
        area = features_2d[:, y_shift:image_height, x_shift:image_width, :]
        flatten_area = tf.reshape(area, [batch_size, -1, depth, 1])
        images.append(flatten_area)
    image_tensor = tf.concat(images, axis=3)
    max_tensor = fn(image_tensor, axis=3)
  return max_tensor