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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 classify(self, images_file=None, images_filename=None, images_file_content_type=None, url=None, threshold=None, owners=None, classifier_ids=None, accept_language=None, **kwargs):
 """ Classify images. Classify images with built-in or custom classifiers. :param file images_file: An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters. You can also include an image with the **url** parameter. :param str images_filename: The filename for images_file. :param str images_file_content_type: The content type of images_file. :param str url: The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB. You can also include images with the **images_file** parameter. :param float threshold: The minimum score a class must have to be displayed in the response. Set the threshold to `0.0` to return all identified classes. :param list[str] owners: The categories of classifiers to apply. The **classifier_ids** parameter overrides **owners**, so make sure that **classifier_ids** is empty. - Use `IBM` to classify against the `default` general classifier. You get the same result if both **classifier_ids** and **owners** parameters are empty. - Use `me` to classify against all your custom classifiers. However, for better performance use **classifier_ids** to specify the specific custom classifiers to apply. - Use both `IBM` and `me` to analyze the image against both classifier categories. :param list[str] classifier_ids: Which classifiers to apply. Overrides the **owners** parameter. You can specify both custom and built-in classifier IDs. The built-in `default` classifier is used if both **classifier_ids** and **owners** parameters are empty. The following built-in classifier IDs require no training: - `default`: Returns classes from thousands of general tags. - `food`: Enhances specificity and accuracy for images of food items. - `explicit`: Evaluates whether the image might be pornographic. :param str accept_language: The desired language of parts of the response. See the response for details. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """


        headers = {'Accept-Language': accept_language}
        if 'headers' in kwargs:
            headers.update(kwargs.get('headers'))
        sdk_headers = get_sdk_headers('watson_vision_combined', 'V3',
                                      'classify')
        headers.update(sdk_headers)

        params = {'version': self.version}

        form_data = {}
        if images_file:
            if not images_filename and hasattr(images_file, 'name'):
                images_filename = basename(images_file.name)
            if not images_filename:
                raise ValueError('images_filename must be provided')
            form_data['images_file'] = (images_filename, images_file,
                                        images_file_content_type or
                                        'application/octet-stream')
        if url:
            form_data['url'] = (None, url, 'text/plain')
        if threshold:
            form_data['threshold'] = (None, threshold, 'application/json')
        if owners:
            owners = self._convert_list(owners)
            form_data['owners'] = (None, owners, 'application/json')
        if classifier_ids:
            classifier_ids = self._convert_list(classifier_ids)
            form_data['classifier_ids'] = (None, classifier_ids,
                                           'application/json')

        url = '/v3/classify'
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            files=form_data,
            accept_json=True)
        return response