class_docstr: DPQuery to estimate target quantile of a univariate distribution.
  
  Uses 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.initial_global_state`.
docstr_tok: ["Implements","`","tensorflow_privacy.DPQuery.initial_global_state","`","."]
code: def initial_global_state(self):
      """Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
      return self._GlobalState(
          tf.cast(self._initial_estimate, tf.float32),
          tf.cast(self._target_quantile, tf.float32),
          tf.cast(self._learning_rate, tf.float32),
          self._below_estimate_query.initial_global_state(),
      )
  
imports: import collections
  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 no_privacy_query
  from tensorflow_privacy.privacy.dp_query import normalized_query
  from tensorflow_privacy.privacy.dp_query import tree_aggregation_query
label_desc: Use image node_id 3 for calling the QuantileEstimatorQuery obj's underlying member method code with example usage: obj.initial_global_state() and returns: self