class_docstr: `DPQuery` for Gaussian sum queries with adaptive clipping.
  
  Clipping norm is tuned adaptively to converge to a value such that a specified
  quantile of updates are clipped, using the algorithm of Andrew et al. (
  https://arxiv.org/abs/1905.03871). See the paper for details and suggested
  hyperparameter settings.
class_docstr_tok: ["`","DPQuery","`","for","Gaussian","sum","queries","with","adaptive","clipping",".","Clipping","norm","is","tuned","adaptively","to","converge","to","a","value","such","that","a","specified","quantile","of","updates","are","clipped",",","using","the","algorithm","of","Andrew","et","al",".","(","https",":","\/\/arxiv.org\/abs\/1905.03871",")",".","See","the","paper","for","details","and","suggested","hyperparameter","settings","."]
docstr: Implements `tensorflow_privacy.DPQuery.derive_sample_params`.
docstr_tok: ["Implements","`","tensorflow_privacy.DPQuery.derive_sample_params","`","."]
code: def derive_sample_params(self, global_state):
      """Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
      return self._SampleParams(
          self._sum_query.derive_sample_params(global_state.sum_state),
          self._quantile_estimator_query.derive_sample_params(
              global_state.quantile_estimator_state
          ),
      )
  
file_path: privacy/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py
imports: import collections
  import dp_accounting
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import dp_query
  from tensorflow_privacy.privacy.dp_query import gaussian_query
  from tensorflow_privacy.privacy.dp_query import quantile_estimator_query
label_desc: Use image node_id 3 for calling the QuantileAdaptiveClipSumQuery obj's underlying member method code with example usage: obj.derive_sample_params(global_state) and returns: self