class_docstr: Wraps inputs into a Keras Sequence to allow Keras models to predict on
  arbitrary inputs which may be out of memory.
code: def __init__(self, X, batch_size=32):
      self.X = X
      self.batch_size = batch_size
      self.placeholder_labels = np.empty(batch_size, dtype=np.uint8)
      self.batched_data = hasattr(X, "get_batch")
  
file_path: ivis/ivis/data/generators/generators.py
imports: import tensorflow
  import numpy
  from scipy.sparse import issparse
label_desc: Use image node_id 1 to create a new KerasSequence object from inherited base classes: tf.keras.utils with example: obj = KerasSequence(X, batch_size)