code: def multi_augment(
      self,
      sample: Sample,
      n_augmentations=1,
      include_non_augmented=True,
  ):
      if include_non_augmented:
          out = [sample]
      else:
          out = []
  
      for n in range(n_augmentations):
          meta = copy.deepcopy(sample.meta)
          l, t = self.augment(sample.inputs, sample.targets, meta)
          out.append(Sample(inputs=l, targets=t, meta=meta))
  
      return out
  
imports: import copy
  from dataclasses import dataclass, field
  from functools import partial
  from typing import List, Iterable, Type
  import numpy
  from paiargparse import pai_dataclass, pai_meta
  from tfaip.data.pipeline.definitions import Sample
  from tfaip.data.pipeline.processor.dataprocessor import MappingDataProcessor, DataProcessorParams
  from tfaip.util.multiprocessing.parallelmap import parallel_map
  from calamari_ocr.ocr.augmentation.data_augmenter import DataAugmenterParams, DefaultDataAugmenterParams
  from calamari_ocr.ocr.augmentation.dataaugmentationparams import DataAugmentationAmount
label_desc: Use image node_id 5 for calling the AugmentationProcessor obj's underlying member method code with example usage: obj.multi_augment(sample, n_augmentations, include_non_augmented) and returns: out