class_docstr: Data generator with an optional increasing batch size.
class_docstr_tok: ["Data","generator","with","an","optional","increasing","batch","size","."]
docstr: Initializes the increasing batch size data generator.
  
  Args:
      model_data: The model data to use.
      batch_size: The batch size.
      epochs: The total number of epochs.
      batch_strategy: The batch strategy.
      shuffle: If 'True', data will be shuffled.
docstr_tok: ["Initializes","the","increasing","batch","size","data","generator",".","Args",":","model_data",":","The","model","data","to","use",".","batch_size",":","The","batch","size",".","epochs",":","The","total","number","of","epochs",".","batch_strategy",":","The","batch","strategy",".","shuffle",":","If","'True","'",",","data","will","be","shuffled","."]
code: def __init__(
      self,
      model_data: RasaModelData,
      batch_size: Union[List[int], int],
      epochs: int = 1,
      batch_strategy: Text = SEQUENCE,
      shuffle: bool = True,
  ):
      """Initializes the increasing batch size data generator.
  
      Args:
          model_data: The model data to use.
          batch_size: The batch size.
          epochs: The total number of epochs.
          batch_strategy: The batch strategy.
          shuffle: If 'True', data will be shuffled.
      """
      super().__init__(model_data, batch_size, batch_strategy, shuffle)
  
      if isinstance(batch_size, list):
          logger.debug(
              "The provided batch size is a list, this data generator will use a "
              "linear increasing batch size."
          )
  
      self._epochs = epochs
      # we use `on_epoch_end` method to prepare data for the next epoch
      # set current epoch to `-1`, so that `on_epoch_end` will increase it to `0`
      self._current_epoch = -1
      # actual batch size will be set inside `on_epoch_end`
      self._current_batch_size = 0
      # create separate data variable that will store modified data for each batch
      self._data: Data = {}
      self.on_epoch_end()
  
imports: import math
  from typing import List, Union, Text, Optional, Any, Tuple, Dict, cast
  import logging
  import scipy.sparse
  import numpy
  from tensorflow.keras.utils import Sequence
  from rasa.utils.tensorflow.constants import SEQUENCE, BALANCED
  from rasa.utils.tensorflow.model_data import RasaModelData, Data, FeatureArray
label_desc: Use image node_id 1 to create a new RasaBatchDataGenerator object from inherited base classes: RasaDataGenerator with example: obj = RasaBatchDataGenerator(model_data, batch_size, epochs, batch_strategy, shuffle)