class_docstr: Diet 2 Diet transformer implementation.
class_docstr_tok: ["Diet","2","Diet","transformer","implementation","."]
docstr: Calculates the loss for the given batch.
  
  Args:
      batch_in: The batch.
  
  Returns:
      The loss of the given batch.
code: def batch_loss(
      self,
      batch_in: Union[Tuple[tf.Tensor, ...], Tuple[np.ndarray, ...]],
  ) -> tf.Tensor:
      """Calculates the loss for the given batch.
  
      Args:
          batch_in: The batch.
  
      Returns:
          The loss of the given batch.
      """
      tf_batch_data = self.batch_to_model_data_format(
          batch_in, self.data_signature
      )
  
      # Process all features for text.
      sequence_feature_lengths_text = (
          self._get_sequence_feature_lengths(tf_batch_data, TEXT)
      )
      (
          text_transformed,
          text_in,
          _,
          text_seq_ids,
          mlm_mask_booleanean_text,
          _,
      ) = self._tf_layers[f"sequence_layer.{self.text_name}"](
          (
              tf_batch_data[TEXT][SEQUENCE],
              tf_batch_data[TEXT][SENTENCE],
              sequence_feature_lengths_text,
          ),
          training=self._training,
      )
  
      # Process all features for labels.
      sequence_feature_lengths_label = (
          self._get_sequence_feature_lengths(tf_batch_data, LABEL)
      )
      label_transformed, _, _, _, _, _ = self._tf_layers[
          f"sequence_layer.{self.label_name}"
      ](
          (
              tf_batch_data[LABEL][SEQUENCE],
              tf_batch_data[LABEL][SENTENCE],
              sequence_feature_lengths_label,
          ),
          training=self._training,
      )
  
      losses = []
  
      if self.config[MASKED_LM]:
          loss, acc = self._mask_loss(
              text_transformed,
              text_in,
              text_seq_ids,
              mlm_mask_booleanean_text,
              self.text_name,
          )
  
          self.mask_loss.update_state(loss)
          self.mask_acc.update_state(acc)
          losses.append(loss)
  
      # Get sentence feature vector for label classification. The vector is extracted
      # from the last position with real features. To determine this position, we
      # combine the sequence lengths of sequence- and sentence-level features.
      sentence_feature_lengths_text = (
          self._get_sentence_feature_lengths(tf_batch_data, TEXT)
      )
      sentence_vector_text = self._last_token(
          text_transformed,
          sequence_feature_lengths_text + sentence_feature_lengths_text,
      )
  
      # Extract sentence vector for the label attribute in the same way.
      sentence_feature_lengths_label = (
          self._get_sentence_feature_lengths(tf_batch_data, LABEL)
      )
      sentence_vector_label = self._last_token(
          label_transformed,
          sequence_feature_lengths_label
          + sentence_feature_lengths_label,
      )
      label_ids = tf_batch_data[LABEL_KEY][LABEL_SUB_KEY][0]
  
      loss, acc = self._calculate_label_loss(
          sentence_vector_text, sentence_vector_label, label_ids
      )
      self.response_loss.update_state(loss)
      self.response_acc.update_state(acc)
      losses.append(loss)
  
      return tf.math.add_n(losses)
  
imports: from __future__ import annotations
  import copy
  import logging
  from rasa.nlu.featurizers.featurizer import Featurizer
  import numpy
  import tensorflow
  from typing import Any, Dict, Optional, Text, Tuple, Union, List, Type
  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.shared.constants import DIAGNOSTIC_DATA
  from rasa.shared.nlu.training_data import util
  import rasa.shared.utils.io
  from rasa.shared.exceptions import InvalidConfigException
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.classifiers.diet_classifier import DIET, LABEL_KEY, LABEL_SUB_KEY, SENTENCE, SEQUENCE, DIETClassifier
  from rasa.nlu.extractors.extractor import EntityTagSpec
  from rasa.utils.tensorflow import rasa_layers
  from rasa.utils.tensorflow.constants import LABEL, HIDDEN_LAYERS_SIZES, SHARE_HIDDEN_LAYERS, TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS, NUM_HEADS, BATCH_SIZES, BATCH_STRATEGY, EPOCHS, RANDOM_SEED, LEARNING_RATE, RANKING_LENGTH, RENORMALIZE_CONFIDENCES, LOSS_TYPE, SIMILARITY_TYPE, NUM_NEG, SPARSE_INPUT_DROPOUT, DENSE_INPUT_DROPOUT, MASKED_LM, ENTITY_RECOGNITION, INTENT_CLASSIFICATION, EVAL_NUM_EXAMPLES, EVAL_NUM_EPOCHS, UNIDIRECTIONAL_ENCODER, DROP_RATE, DROP_RATE_ATTENTION, CONNECTION_DENSITY, NEGATIVE_MARGIN_SCALE, REGULARIZATION_CONSTANT, SCALE_LOSS, USE_MAX_NEG_SIM, MAX_NEG_SIM, MAX_POS_SIM, EMBEDDING_DIMENSION, BILOU_FLAG, KEY_RELATIVE_ATTENTION, VALUE_RELATIVE_ATTENTION, MAX_RELATIVE_POSITION, RETRIEVAL_INTENT, USE_TEXT_AS_LABEL, CROSS_ENTROPY, AUTO, BALANCED, TENSORBOARD_LOG_DIR, TENSORBOARD_LOG_LEVEL, CONCAT_DIMENSION, FEATURIZERS, CHECKPOINT_MODEL, DENSE_DIMENSION, CONSTRAIN_SIMILARITIES, MODEL_CONFIDENCE, SOFTMAX
  from rasa.nlu.constants import RESPONSE_SELECTOR_PROPERTY_NAME, RESPONSE_SELECTOR_RETRIEVAL_INTENTS, RESPONSE_SELECTOR_RESPONSES_KEY, RESPONSE_SELECTOR_PREDICTION_KEY, RESPONSE_SELECTOR_RANKING_KEY, RESPONSE_SELECTOR_UTTER_ACTION_KEY, RESPONSE_SELECTOR_DEFAULT_INTENT, DEFAULT_TRANSFORMER_SIZE
  from rasa.shared.nlu.constants import TEXT, INTENT, RESPONSE, INTENT_RESPONSE_KEY, INTENT_NAME_KEY, PREDICTED_CONFIDENCE_KEY
  from rasa.utils.tensorflow.model_data import RasaModelData
  from rasa.utils.tensorflow.models import RasaModel
label_desc: Use image node_id 7 for calling the DIET2DIET obj's underlying member method code with example usage: obj.batch_loss(batch_in) and returns: tf