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 __getitem__(self, index):
      batch_indices = range(
          index * self.batch_size,
          min((index + 1) * self.batch_size, self.X.shape[0]),
      )
  
      if self.batched_data:
          batch = self.X.get_batch(batch_indices)
      else:
          batch = [self.X[i] for i in batch_indices]
  
      if issparse(self.X):
          batch = [ele.toarray() for ele in batch]
          batch = np.squeeze(batch)
  
      placeholder_labels = self.placeholder_labels[: len(batch_indices)]
      return np.asarray(batch), placeholder_labels
  
file_path: ivis/ivis/data/generators/generators.py
imports: import tensorflow
  import numpy
  from scipy.sparse import issparse
label_desc: Use image node_id 3 for calling the KerasSequence obj's underlying member method code with example usage: obj.__getitem__(index) and returns: np,  placeholder_labels