docs: def image_recognition(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
      """
      Given an input image, returns a dictionary of image classifications with associated scores
  
      * Input can be either grayscale or rgb color and should either be a numpy array or nested list format.
      * Input data should be either uint8 0-255 range values or floating point between 0 and 1.
      * Large images (i.e. 1024x768+) are much bigger than needed, minaxis resizing will be done internally to 144 if needed.
      * For ideal performance, images should be square aspect ratio but non-square aspect ratios are supported as well.
  
      Example usage:
  
      .. code-block:: python
  
         >>> from indicoio import image_recognition
         >>> features = image_recognition(<filename>)
  
      :param image: The image to be analyzed.
      :type image: str
      :rtype: dict containing classifications
      """
      image = data_preprocess(image, batch=batch, size=144, min_axis=True)
      url_params = {"batch": batch, "api_key": api_key, "version": version}
      return api_handler(image, cloud=cloud, api="imagerecognition", url_params=url_params, **kwargs)