code: def main():
      nb_rows = 8
      nb_cols = 8
      h, w = (128, 128)
      sample_size = 128
  
      noise_gens = [
          iap.SimplexNoise(),
          iap.FrequencyNoise(
              exponent=-4,
              size_px_max=sample_size,
              upscale_method="cubic",
          ),
          iap.FrequencyNoise(
              exponent=-2,
              size_px_max=sample_size,
              upscale_method="cubic",
          ),
          iap.FrequencyNoise(
              exponent=0,
              size_px_max=sample_size,
              upscale_method="cubic",
          ),
          iap.FrequencyNoise(
              exponent=2,
              size_px_max=sample_size,
              upscale_method="cubic",
          ),
          iap.FrequencyNoise(
              exponent=4,
              size_px_max=sample_size,
              upscale_method="cubic",
          ),
          iap.FrequencyNoise(
              exponent=(-4, 4),
              size_px_max=(4, sample_size),
              upscale_method=["nearest", "linear", "cubic"],
          ),
          iap.IterativeNoiseAggregator(
              other_param=iap.FrequencyNoise(
                  exponent=(-4, 4),
                  size_px_max=(4, sample_size),
                  upscale_method=["nearest", "linear", "cubic"],
              ),
              iterations=(1, 3),
              aggregation_method=["max", "avg"],
          ),
          iap.IterativeNoiseAggregator(
              other_param=iap.Sigmoid(
                  iap.FrequencyNoise(
                      exponent=(-4, 4),
                      size_px_max=(4, sample_size),
                      upscale_method=["nearest", "linear", "cubic"],
                  ),
                  threshold=(-10, 10),
                  activated=0.33,
                  mul=20,
                  add=-10,
              ),
              iterations=(1, 3),
              aggregation_method=["max", "avg"],
          ),
      ]
  
      samples = [[] for _ in range(len(noise_gens))]
      for _ in range(nb_rows * nb_cols):
          for i, noise_gen in enumerate(noise_gens):
              samples[i].append(noise_gen.draw_samples((h, w)))
  
      rows = [np.hstack(row) for row in samples]
      grid = np.vstack(rows)
      ia.imshow((grid * 255).astype(np.uint8))
  
      images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
      seqs = [
          iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
          iaa.SimplexNoiseAlpha(
              first=iaa.EdgeDetect(1.0), per_channel=True
          ),
          iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
          iaa.FrequencyNoiseAlpha(
              first=iaa.EdgeDetect(1.0), per_channel=True
          ),
      ]
      images_aug = []
  
      for seq in seqs:
          images_aug.append(np.hstack(seq.augment_images(images)))
      images_aug = np.vstack(images_aug)
      ia.imshow(images_aug)
  
imports: from __future__ import print_function, division
  import numpy
  import imgaug
  from imgaug import augmenters
  from imgaug import parameters
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types