code: def _prepare_dot_product_loss(
      self, name: Text, scale_loss: bool, prefix: Text = "loss"
  ) -> None:
      self._tf_layers[f"{prefix}.{name}"] = self.dot_product_loss_layer(
          self.config[NUM_NEG],
          loss_type=self.config[LOSS_TYPE],
          mu_pos=self.config[MAX_POS_SIM],
          mu_neg=self.config[MAX_NEG_SIM],
          use_max_sim_neg=self.config[USE_MAX_NEG_SIM],
          neg_lambda=self.config[NEGATIVE_MARGIN_SCALE],
          scale_loss=scale_loss,
          similarity_type=self.config[SIMILARITY_TYPE],
          constrain_similarities=self.config[CONSTRAIN_SIMILARITIES],
          model_confidence=self.config[MODEL_CONFIDENCE],
      )
  
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 13 for calling the TransformerRasaModel obj's underlying member method code with example usage: obj._prepare_dot_product_loss(name, scale_loss, prefix) without return types