class_docstr: Implements DPQuery interface for Gaussian sum queries.
  
  Clips records to bound the L2 norm, then adds Gaussian noise to the sum.
docstr: Clips the l2 norm, returning the clipped record and the l2 norm.
  
  Args:
    params: The parameters for the sample.
    record: The record to be processed.
  
  Returns:
    A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is
      the structure of preprocessed tensors, and l2_norm is the total l2 norm
      before clipping.
docstr_tok: ["Clips","the","l2","norm",",","returning","the","clipped","record","and","the","l2","norm",".","Args",":","params",":","The","parameters","for","the","sample",".","record",":","The","record","to","be","processed",".","Returns",":","A","tuple","(","preprocessed_records",",","l2_norm",")","where","`","preprocessed_records","`","is","the","structure","of","preprocessed","tensors",",","and","l2_norm","is","the","total","l2","norm","before","clipping","."]
code: def preprocess_record_impl(self, params, record):
      """Clips the l2 norm, returning the clipped record and the l2 norm.
  
      Args:
        params: The parameters for the sample.
        record: The record to be processed.
  
      Returns:
        A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is
          the structure of preprocessed tensors, and l2_norm is the total l2 norm
          before clipping.
      """
      l2_norm_clip = params
      record_as_list = tf.nest.flatten(record)
      clipped_as_list, norm = tf.clip_by_global_norm(
          record_as_list, l2_norm_clip
      )
      return tf.nest.pack_sequence_as(record, clipped_as_list), norm
  
file_path: privacy/tensorflow_privacy/privacy/dp_query/gaussian_query.py
imports: import collections
  import dp_accounting
  import packaging.version
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import dp_query
label_desc: Use image node_id 5 for calling the GaussianSumQuery obj's underlying member method code with example usage: obj.preprocess_record_impl(params, record) and returns: tf,  norm