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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 get_file_clusters(self, this_date, timeout=None):
 """ File similarity clusters for a given time frame. VirusTotal has built its own in-house file similarity clustering functionality. At present, this clustering works only on PE, PDF, DOC and RTF files and is based on a very simple structural feature hash. This hash can very often be confused by certain compression and packing strategies, in other words, this clustering logic is no holly grail, yet it has proven itself very useful in the past. This API offers a programmatic access to the clustering section of VirusTotal Intelligence: https://www.virustotal.com/intelligence/clustering/ NOTE: Please note that you must be logged in with a valid VirusTotal Community user account with access to VirusTotal Intelligence in order to be able to view the clustering listing. :param this_date: A specific day for which we want to access the clustering details, example: 2013-09-10. :param timeout: The amount of time in seconds the request should wait before timing out. :return: JSON object contains several properties num_candidates - Total number of files submitted during the given time frame for which a feature hash could be calculated. num_clusters - Total number of clusters generated for the given time period under consideration, a cluster can be as small as an individual file, meaning that no other feature-wise similar file was found. size_top200	 - The sum of the number of files in the 200 largest clusters identified. clusters - List of JSON objects that contain details about the 200 largest clusters identified. These objects contain 4 properties: id, label, size and avg_positives.. The id field can be used to then query the search API call for files contained in the given cluster. The label property is a verbose human-intelligible name for the cluster. The size field is the number of files that make up the cluster. Finally, avg_positives represents the average number of antivirus detections that the files in the cluster exhibit. """

        params = {'apikey': self.api_key, 'date': this_date}

        try:
            response = requests.get(self.base + 'file/clusters', params=params, proxies=self.proxies, timeout=timeout)
        except requests.RequestException as e:
            return dict(error=str(e))

        return _return_response_and_status_code(response)