class_docstr: Utility class for generating batches of temporal data.
  
  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 [Sequence](/utils/#sequence) instance.
  
  # Examples
  
  ```python
  from keras.preprocessing.sequence import TimeseriesGenerator
  import numpy as np
  
  data = np.array([[i] for i in range(50)])
  targets = np.array([[i] for i in range(50)])
  
  data_gen = TimeseriesGenerator(data, targets,
                                 length=10, sampling_rate=2,
                                 batch_size=2)
  assert len(data_gen) == 20
  
  batch_0 = data_gen[0]
  x, y = batch_0
  assert np.array_equal(x,
                        np.array([[[0], [2], [4], [6], [8]],
                                  [[1], [3], [5], [7], [9]]]))
  assert np.array_equal(y,
                        np.array([[10], [11]]))
  ```
docstr: Returns the TimeseriesGenerator configuration as Python dictionary.
  
  # Returns
      A Python dictionary with the TimeseriesGenerator configuration.
docstr_tok: ["Returns","the","TimeseriesGenerator","configuration","as","Python","dictionary",".","#","Returns","A","Python","dictionary","with","the","TimeseriesGenerator","configuration","."]
code: def get_config(self):
      """Returns the TimeseriesGenerator configuration as Python dictionary.
  
      # Returns
          A Python dictionary with the TimeseriesGenerator configuration.
      """
      data = self.data
      if type(self.data).__module__ == np.__name__:
          data = self.data.tolist()
      try:
          json_data = json.dumps(data)
      except TypeError:
          raise TypeError("Data not JSON Serializable:", data)
  
      targets = self.targets
      if type(self.targets).__module__ == np.__name__:
          targets = self.targets.tolist()
      try:
          json_targets = json.dumps(targets)
      except TypeError:
          raise TypeError("Targets not JSON Serializable:", targets)
  
      return {
          "data": json_data,
          "targets": json_targets,
          "length": self.length,
          "sampling_rate": self.sampling_rate,
          "stride": self.stride,
          "start_index": self.start_index,
          "end_index": self.end_index,
          "shuffle": self.shuffle,
          "reverse": self.reverse,
          "batch_size": self.batch_size,
      }
  
file_path: keras-preprocessing/keras_preprocessing/sequence.py
imports: import json
  import random
  import numpy
label_desc: Use image node_id 4 for calling the TimeseriesGenerator obj's underlying member method code with example usage: obj.get_config() and returns: dict