code: def main():
      params = [
          ("Binomial(0.1)", Binomial(0.1)),
          ("Choice", Choice([0, 1, 2])),
          ("Choice with p", Choice([0, 1, 2], p=[0.1, 0.2, 0.7])),
          ("DiscreteUniform(0, 10)", DiscreteUniform(0, 10)),
          ("Poisson(0)", Poisson(0)),
          ("Poisson(5)", Poisson(5)),
          ("Discretize(Poisson(5))", Discretize(Poisson(5))),
          ("Normal(0, 1)", Normal(0, 1)),
          ("Normal(1, 1)", Normal(1, 1)),
          ("Normal(1, 2)", Normal(0, 2)),
          ("Normal(Choice([-1, 1]), 2)", Normal(Choice([-1, 1]), 2)),
          ("Discretize(Normal(0, 1.0))", Discretize(Normal(0, 1.0))),
          ("Positive(Normal(0, 1.0))", Positive(Normal(0, 1.0))),
          (
              "Positive(Normal(0, 1.0), mode='reroll')",
              Positive(Normal(0, 1.0), mode="reroll"),
          ),
          ("Negative(Normal(0, 1.0))", Negative(Normal(0, 1.0))),
          (
              "Negative(Normal(0, 1.0), mode='reroll')",
              Negative(Normal(0, 1.0), mode="reroll"),
          ),
          ("Laplace(0, 1.0)", Laplace(0, 1.0)),
          ("Laplace(1.0, 3.0)", Laplace(1.0, 3.0)),
          ("Laplace([-1.0, 1.0], 1.0)", Laplace([-1.0, 1.0], 1.0)),
          ("ChiSquare(1)", ChiSquare(1)),
          ("ChiSquare([1, 6])", ChiSquare([1, 6])),
          ("Weibull(0.5)", Weibull(0.5)),
          ("Weibull((1.0, 3.0))", Weibull((1.0, 3.0))),
          ("Uniform(0, 10)", Uniform(0, 10)),
          ("Beta(0.5, 0.5)", Beta(0.5, 0.5)),
          ("Deterministic(1)", Deterministic(1)),
          (
              "Clip(Normal(0, 1), 0, None)",
              Clip(Normal(0, 1), minval=0, maxval=None),
          ),
          ("Multiply(Uniform(0, 10), 2)", Multiply(Uniform(0, 10), 2)),
          ("Add(Uniform(0, 10), 5)", Add(Uniform(0, 10), 5)),
          ("Absolute(Normal(0, 1))", Absolute(Normal(0, 1))),
          ("RandomSign(Poisson(1))", RandomSign(Poisson(1))),
          ("RandomSign(Poisson(1), 0.9)", RandomSign(Poisson(1), 0.9)),
      ]
  
      params_arithmetic = [
          ("Normal(0, 1.0)", Normal(0.0, 1.0)),
          ("Normal(0, 1.0) + 5", Normal(0.0, 1.0) + 5),
          ("5 + Normal(0, 1.0)", 5 + Normal(0.0, 1.0)),
          (
              "5 + Normal(0, 1.0)",
              Add(5, Normal(0.0, 1.0), elementwise=True),
          ),
          ("Normal(0, 1.0) * 10", Normal(0.0, 1.0) * 10),
          ("10 * Normal(0, 1.0)", 10 * Normal(0.0, 1.0)),
          (
              "10 * Normal(0, 1.0)",
              Multiply(10, Normal(0.0, 1.0), elementwise=True),
          ),
          ("Normal(0, 1.0) / 10", Normal(0.0, 1.0) / 10),
          ("10 / Normal(0, 1.0)", 10 / Normal(0.0, 1.0)),
          (
              "10 / Normal(0, 1.0)",
              Divide(10, Normal(0.0, 1.0), elementwise=True),
          ),
          ("Normal(0, 1.0) ** 2", Normal(0.0, 1.0) ** 2),
          ("2 ** Normal(0, 1.0)", 2 ** Normal(0.0, 1.0)),
          (
              "2 ** Normal(0, 1.0)",
              Power(2, Normal(0.0, 1.0), elementwise=True),
          ),
      ]
  
      params_noise = [
          ("SimplexNoise", SimplexNoise()),
          ("Sigmoid(SimplexNoise)", Sigmoid(SimplexNoise())),
          (
              "SimplexNoise(linear)",
              SimplexNoise(upscale_method="linear"),
          ),
          (
              "SimplexNoise(nearest)",
              SimplexNoise(upscale_method="nearest"),
          ),
          ("FrequencyNoise((-4, 4))", FrequencyNoise(exponent=(-4, 4))),
          ("FrequencyNoise(-2)", FrequencyNoise(exponent=-2)),
          ("FrequencyNoise(2)", FrequencyNoise(exponent=2)),
      ]
  
      images_params = [
          param.draw_distribution_graph() for (title, param) in params
      ]
      images_arithmetic = [
          param.draw_distribution_graph()
          for (title, param) in params_arithmetic
      ]
      images_noise = [
          param.draw_distribution_graph(size=(1000, 10, 10))
          for (title, param) in params_noise
      ]
  
      ia.imshow(np.vstack(images_params))
      ia.imshow(np.vstack(images_arithmetic))
      ia.imshow(np.vstack(images_noise))
  
imports: from __future__ import print_function, division
  import imgaug
  from imgaug.parameters import Binomial, Choice, DiscreteUniform, Poisson, Normal, Laplace, ChiSquare, Weibull, Uniform, Beta, Deterministic, Clip, Discretize, Multiply, Add, Divide, Power, Absolute, RandomSign, Positive, Negative, SimplexNoise, FrequencyNoise, Sigmoid
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types