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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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 :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)