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: Constructs data regarding labels to be fed to the model.
  
  The resultant model data should contain the keys `label_intent`, `label`.
  `label_intent` will contain the sequence, sentence and mask features
  for all intent labels and `label` will contain the numerical label ids.
  
  Args:
      attribute_data: Feature data for all intent labels.
      domain: Domain of the assistant.
  
  Returns:
      Features of labels ready to be fed to the model.
docstr_tok: ["Constructs","data","regarding","labels","to","be","fed","to","the","model",".","The","resultant","model","data","should","contain","the","keys","`","label_intent","`",",","`","label","`",".","`","label_intent","`","will","contain","the","sequence",",","sentence","and","mask","features","for","all","intent","labels","and","`","label","`","will","contain","the","numerical","label","ids",".","Args",":","attribute_data",":","Feature","data","for","all","intent","labels",".","domain",":","Domain","of","the","assistant",".","Returns",":","Features","of","labels","ready","to","be","fed","to","the","model","."]
code: def _assemble_label_data(
      self, attribute_data: Data, domain: Domain
  ) -> RasaModelData:
      """Constructs data regarding labels to be fed to the model.
  
      The resultant model data should contain the keys `label_intent`, `label`.
      `label_intent` will contain the sequence, sentence and mask features
      for all intent labels and `label` will contain the numerical label ids.
  
      Args:
          attribute_data: Feature data for all intent labels.
          domain: Domain of the assistant.
  
      Returns:
          Features of labels ready to be fed to the model.
      """
      label_data = RasaModelData()
      label_data.add_data(attribute_data, key_prefix=f"{LABEL_KEY}_")
      label_data.add_lengths(
          f"{LABEL}_{INTENT}",
          SEQUENCE_LENGTH,
          f"{LABEL}_{INTENT}",
          SEQUENCE,
      )
      label_ids = np.arange(len(domain.intents))
      label_data.add_features(
          LABEL_KEY,
          LABEL_SUB_KEY,
          [
              FeatureArray(
                  np.expand_dims(label_ids, -1),
                  number_of_dimensions=2,
              )
          ],
      )
      return label_data
  
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 7 for calling the UnexpecTEDIntentPolicy obj's underlying member method code with example usage: obj._assemble_label_data(attribute_data, domain) and returns: label_data