class_docstr: `UnexpecTEDIntentPolicy` has the same model architecture as `TEDPolicy`.
  
  The difference is at a task level.
  Instead of predicting the next probable action, this policy
  predicts whether the last predicted intent is a likely intent
  according to the training stories and conversation context.
class_docstr_tok: ["`","UnexpecTEDIntentPolicy","`","has","the","same","model","architecture","as","`","TEDPolicy","`",".","The","difference","is","at","a","task","level",".","Instead","of","predicting","the","next","probable","action",",","this","policy","predicts","whether","the","last","predicted","intent","is","a","likely","intent","according","to","the","training","stories","and","conversation","context","."]
docstr: Computes multiple quantiles for each label id.
  
  The quantiles are computed over the negative scores
  collected for each label id. However, no quantile score
  can be greater than the minimum positive score collected
  for the corresponding label id.
  
  Args:
      label_id_scores: Scores collected for each label id
          over positive and negative trackers.
  
  Returns:
      Computed quantiles for each label id.
docstr_tok: ["Computes","multiple","quantiles","for","each","label","id",".","The","quantiles","are","computed","over","the","negative","scores","collected","for","each","label","id",".","However",",","no","quantile","score","can","be","greater","than","the","minimum","positive","score","collected","for","the","corresponding","label","id",".","Args",":","label_id_scores",":","Scores","collected","for","each","label","id","over","positive","and","negative","trackers",".","Returns",":","Computed","quantiles","for","each","label","id","."]
code: def _compute_label_quantiles(
      label_id_scores: Dict[int, Dict[Text, List[float]]]
  ) -> Dict[int, List[float]]:
      """Computes multiple quantiles for each label id.
  
      The quantiles are computed over the negative scores
      collected for each label id. However, no quantile score
      can be greater than the minimum positive score collected
      for the corresponding label id.
  
      Args:
          label_id_scores: Scores collected for each label id
              over positive and negative trackers.
  
      Returns:
          Computed quantiles for each label id.
      """
      label_quantiles = {}
  
      quantile_indices = [
          1 - tolerance_value / 100.0
          for tolerance_value in range(0, 100, 5)
      ]
      for label_id, prediction_scores in label_id_scores.items():
          positive_scores, negative_scores = (
              prediction_scores[POSITIVE_SCORES_KEY],
              prediction_scores[NEGATIVE_SCORES_KEY],
          )
          minimum_positive_score = min(positive_scores)
          if negative_scores:
              quantile_values = np.quantile(  # type: ignore[call-overload]
                  negative_scores,
                  quantile_indices,
                  interpolation="lower",
              )
              label_quantiles[label_id] = [
                  min(minimum_positive_score, value)
                  for value in quantile_values
              ]
          else:
              label_quantiles[label_id] = [
                  minimum_positive_score
              ] * len(quantile_indices)
  
      return label_quantiles
  
imports: import dataclasses
  import logging
  from pathlib import Path
  from typing import Any, List, Optional, Text, Dict, Type, Union
  import numpy
  import tensorflow
  import rasa.utils.common
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.classifiers import LABEL_RANKING_LENGTH
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.core.domain import Domain
  from rasa.shared.core.trackers import DialogueStateTracker
  from rasa.shared.core.constants import SLOTS, ACTIVE_LOOP, ACTION_UNLIKELY_INTENT_NAME
  from rasa.shared.core.events import UserUttered, ActionExecuted
  from rasa.shared.nlu.constants import INTENT, TEXT, ENTITIES, ACTION_NAME, SPLIT_ENTITIES_BY_COMMA, SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE
  from rasa.nlu.extractors.extractor import EntityTagSpec
  from rasa.core.featurizers.precomputation import MessageContainerForCoreFeaturization
  from rasa.core.featurizers.tracker_featurizers import TrackerFeaturizer
  from rasa.core.featurizers.tracker_featurizers import IntentMaxHistoryTrackerFeaturizer
  from rasa.core.featurizers.single_state_featurizer import IntentTokenizerSingleStateFeaturizer
  from rasa.shared.core.generator import TrackerWithCachedStates
  from rasa.core.constants import DIALOGUE, POLICY_MAX_HISTORY, POLICY_PRIORITY, UNLIKELY_INTENT_POLICY_PRIORITY
  from rasa.core.policies.policy import PolicyPrediction
  from rasa.core.policies.ted_policy import LABEL_KEY, LABEL_SUB_KEY, TEDPolicy, TED, SEQUENCE_LENGTH, SEQUENCE, PREDICTION_FEATURES
  from rasa.utils import train_utils
  from rasa.utils.tensorflow.models import RasaModel
  from rasa.utils.tensorflow.constants import LABEL, DENSE_DIMENSION, ENCODING_DIMENSION, UNIDIRECTIONAL_ENCODER, TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS, NUM_HEADS, BATCH_SIZES, BATCH_STRATEGY, EPOCHS, RANDOM_SEED, RANKING_LENGTH, LOSS_TYPE, SIMILARITY_TYPE, NUM_NEG, EVAL_NUM_EXAMPLES, EVAL_NUM_EPOCHS, REGULARIZATION_CONSTANT, SCALE_LOSS, EMBEDDING_DIMENSION, DROP_RATE_DIALOGUE, DROP_RATE_LABEL, DROP_RATE, DROP_RATE_ATTENTION, CONNECTION_DENSITY, KEY_RELATIVE_ATTENTION, VALUE_RELATIVE_ATTENTION, MAX_RELATIVE_POSITION, INNER, BALANCED, TENSORBOARD_LOG_DIR, TENSORBOARD_LOG_LEVEL, CHECKPOINT_MODEL, FEATURIZERS, ENTITY_RECOGNITION, IGNORE_INTENTS_LIST, BILOU_FLAG, LEARNING_RATE, CROSS_ENTROPY, SPARSE_INPUT_DROPOUT, DENSE_INPUT_DROPOUT, MASKED_LM, HIDDEN_LAYERS_SIZES, CONCAT_DIMENSION, TOLERANCE, LABEL_PAD_ID, POSITIVE_SCORES_KEY, NEGATIVE_SCORES_KEY, USE_GPU
  from rasa.utils.tensorflow import layers
  from rasa.utils.tensorflow.model_data import RasaModelData, FeatureArray, Data
  import rasa.utils.io
  from rasa.core.exceptions import RasaCoreException
  from rasa.shared.utils import common
label_desc: Use image node_id 18 for calling the UnexpecTEDIntentPolicy obj's underlying member method code with example usage: obj._compute_label_quantiles(label_id_scores) and returns: label_quantiles