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]]))
  ```
code: def __getitem__(self, index):
      if self.shuffle:
          rows = np.random.randint(
              self.start_index, self.end_index + 1, size=self.batch_size
          )
      else:
          i = self.start_index + self.batch_size * self.stride * index
          rows = np.arange(
              i,
              min(
                  i + self.batch_size * self.stride, self.end_index + 1
              ),
              self.stride,
          )
  
      samples = np.array(
          [
              self.data[row - self.length : row : self.sampling_rate]
              for row in rows
          ]
      )
      targets = np.array([self.targets[row] for row in rows])
  
      if self.reverse:
          return samples[:, ::-1, ...], targets
      return samples, targets
  
file_path: keras-preprocessing/keras_preprocessing/sequence.py
imports: import json
  import random
  import numpy
label_desc: Use image node_id 3 for calling the TimeseriesGenerator obj's underlying member method code with example usage: obj.__getitem__(index) and returns: samples,  targets, samples,  targets