docstr: Fetches the sequence lengths of real tokens per input example.
  
  The number of real tokens for an example is the same as the length of the
  sequence of the sequence-level (token-level) features for that input example.
docstr_tok: ["Fetches","the","sequence","lengths","of","real","tokens","per","input","example",".","The","number","of","real","tokens","for","an","example","is","the","same","as","the","length","of","the","sequence","of","the","sequence-level","(","token-level",")","features","for","that","input","example","."]
code: def _get_sequence_feature_lengths(
      self,
      tf_batch_data: Dict[Text, Dict[Text, List[tf.Tensor]]],
      key: Text,
  ) -> tf.Tensor:
      """Fetches the sequence lengths of real tokens per input example.
  
      The number of real tokens for an example is the same as the length of the
      sequence of the sequence-level (token-level) features for that input example.
      """
      if key in tf_batch_data and SEQUENCE_LENGTH in tf_batch_data[key]:
          return tf.cast(
              tf_batch_data[key][SEQUENCE_LENGTH][0], dtype=tf.int32
          )
  
      batch_dim = self._get_batch_dim(tf_batch_data[key])
      return tf.zeros([batch_dim], dtype=tf.int32)
  
imports: import time
  import random
  import tensorflow
  import numpy
  import logging
  import os
  from collections import defaultdict
  from typing import List, Text, Dict, Tuple, Union, Optional, Any, TYPE_CHECKING
  from keras.utils import tf_utils
  from keras import Model
  from rasa.shared.constants import DIAGNOSTIC_DATA
  from rasa.utils.tensorflow.constants import LABEL, IDS, INTENT_CLASSIFICATION, SENTENCE, SEQUENCE_LENGTH, RANDOM_SEED, EMBEDDING_DIMENSION, REGULARIZATION_CONSTANT, SIMILARITY_TYPE, CONNECTION_DENSITY, NUM_NEG, LOSS_TYPE, MAX_POS_SIM, MAX_NEG_SIM, USE_MAX_NEG_SIM, NEGATIVE_MARGIN_SCALE, SCALE_LOSS, LEARNING_RATE, CONSTRAIN_SIMILARITIES, MODEL_CONFIDENCE, RUN_EAGERLY
  from rasa.utils.tensorflow.model_data import RasaModelData, FeatureSignature, FeatureArray
  import rasa.utils.train_utils
  from rasa.utils.tensorflow import layers
  from rasa.utils.tensorflow import rasa_layers
  from rasa.utils.tensorflow.data_generator import RasaDataGenerator, RasaBatchDataGenerator
  from rasa.shared.nlu.constants import TEXT
  from rasa.shared.exceptions import RasaException
  from rasa.utils.tensorflow.types import BatchData, MaybeNestedBatchData
label_desc: Use image node_id 18 for calling the TransformerRasaModel obj's underlying member method code with example usage: obj._get_sequence_feature_lengths(tf_batch_data, key) and returns: tf, tf