class_docstr: Utility class for generating batches of temporal data.
  
  DEPRECATED.
  
  This class takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  stride, length of history, etc., to produce batches for
  training/validation.
  
  Arguments:
      data: Indexable generator (such as list or Numpy array)
          containing consecutive data points (timesteps).
          The data should be at 2D, and axis 0 is expected
          to be the time dimension.
      targets: Targets corresponding to timesteps in `data`.
          It should have same length as `data`.
      length: Length of the output sequences (in number of timesteps).
      sampling_rate: Period between successive individual timesteps
          within sequences. For rate `r`, timesteps
          `data[i]`, `data[i-r]`, ... `data[i - length]`
          are used for create a sample sequence.
      stride: Period between successive output sequences.
          For stride `s`, consecutive output samples would
          be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
      start_index: Data points earlier than `start_index` will not be used
          in the output sequences. This is useful to reserve part of the
          data for test or validation.
      end_index: Data points later than `end_index` will not be used
          in the output sequences. This is useful to reserve part of the
          data for test or validation.
      shuffle: Whether to shuffle output samples,
          or instead draw them in chronological order.
      reverse: Boolean: if `true`, timesteps in each output sample will be
          in reverse chronological order.
      batch_size: Number of timeseries samples in each batch
          (except maybe the last one).
  
  Returns:
      A PyDataset instance.
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docstr: Returns a JSON string containing the generator's configuration.
  
  Args:
      **kwargs: Additional keyword arguments to be passed
          to `json.dumps()`.
  
  Returns:
      A JSON string containing the tokenizer configuration.
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code: def to_json(self, **kwargs):
      """Returns a JSON string containing the generator's configuration.
  
      Args:
          **kwargs: Additional keyword arguments to be passed
              to `json.dumps()`.
  
      Returns:
          A JSON string containing the tokenizer configuration.
      """
      config = self.get_config()
      timeseries_generator_config = {
          "class_name": self.__class__.__name__,
          "config": config,
      }
      return json.dumps(timeseries_generator_config, **kwargs)
  
file_path: keras/keras/legacy/preprocessing/sequence.py
imports: import json
  import random
  import numpy
  from keras.api_export import keras_export
  from keras.trainers.data_adapters.py_dataset_adapter import PyDataset
label_desc: Use image node_id 5 for calling the TimeseriesGenerator obj's underlying member method code with example usage: obj.to_json() and returns: json