class_docstr: Base class for image data iterators.
  
  Every `Iterator` must implement the `_get_batches_of_transformed_samples`
  method.
  
  # Arguments
      n: Integer, total number of samples in the dataset to loop over.
      batch_size: Integer, size of a batch.
      shuffle: Boolean, whether to shuffle the data between epochs.
      seed: Random seeding for data shuffling.
docstr: Gets a batch of transformed samples.
  
  # Arguments
      index_array: Array of sample indices to include in batch.
  
  # Returns
      A batch of transformed samples.
docstr_tok: ["Gets","a","batch","of","transformed","samples",".","#","Arguments","index_array",":","Array","of","sample","indices","to","include","in","batch",".","#","Returns","A","batch","of","transformed","samples","."]
code: def _get_batches_of_transformed_samples(self, index_array):
      """Gets a batch of transformed samples.
  
      # Arguments
          index_array: Array of sample indices to include in batch.
  
      # Returns
          A batch of transformed samples.
      """
      raise NotImplementedError
  
imports: import os
  import threading
  import numpy
  from keras_preprocessing import get_keras_submodule
  from .utils import array_to_img, img_to_array, load_img
label_desc: Use image node_id 11 for calling the Iterator obj's underlying member method code with example usage: obj._get_batches_of_transformed_samples(index_array) without return types