code: def make_xor_data_sequences(
      shuffle: bool = False,
      seed: Optional[int] = None,
      dtype: Type[Any] = np.int64,
      multi_input_output: bool = False,
      batch_size: int = 1,
  ) -> Tuple[keras_utils.Sequence, keras_utils.Sequence]:
      """
      Generates data loaders for the toy XOR problem.  The dataset only has four
      possible inputs.  For the purposes of testing, the validation set is the
      same as the training dataset.
      """
      training_data, training_labels = xor_data(dtype)
  
      if shuffle:
          if seed is not None:
              np.random.seed(seed)
          idxs = np.random.permutation(4)
          training_data = training_data[idxs]
          training_labels = training_labels[idxs]
  
      return (
          keras._ArrayLikeAdapter(
              training_data, training_labels, batch_size=batch_size
          ),
          keras._ArrayLikeAdapter(
              training_data, training_labels, batch_size=batch_size
          ),
      )
  
imports: from typing import Any, Optional, Tuple, Type
  import numpy
  from tensorflow.keras import utils
  from determined import keras
label_desc: Use image node_id 2 for calling a global function with example usage: make_xor_data_sequences(shuffle, seed, dtype, multi_input_output, batch_size) and returns: keras,  keras