code: def test_random_all_with_class_methods(generator_class, sz):
      def rnd_test(func, *args, **kwargs):
          a = func(*args, **kwargs)
          assert type(a._meta) == np.ndarray
          assert_eq(
              a, a
          )  # Check that _meta and computed arrays match types
  
      rnd_test(generator_class().beta, 1, 2, size=sz, chunks=3)
      rnd_test(generator_class().binomial, 10, 0.5, size=sz, chunks=3)
      rnd_test(generator_class().chisquare, 1, size=sz, chunks=3)
      rnd_test(generator_class().exponential, 1, size=sz, chunks=3)
      rnd_test(generator_class().f, 1, 2, size=sz, chunks=3)
      rnd_test(generator_class().gamma, 5, 1, size=sz, chunks=3)
      rnd_test(generator_class().geometric, 1, size=sz, chunks=3)
      rnd_test(generator_class().gumbel, 1, size=sz, chunks=3)
      rnd_test(
          generator_class().hypergeometric, 1, 2, 3, size=sz, chunks=3
      )
      rnd_test(generator_class().laplace, size=sz, chunks=3)
      rnd_test(generator_class().logistic, size=sz, chunks=3)
      rnd_test(generator_class().lognormal, size=sz, chunks=3)
      rnd_test(generator_class().logseries, 0.5, size=sz, chunks=3)
      rnd_test(
          generator_class().multinomial,
          20,
          [1 / 6.0] * 6,
          size=sz,
          chunks=3,
      )
      rnd_test(
          generator_class().negative_binomial, 5, 0.5, size=sz, chunks=3
      )
      rnd_test(
          generator_class().noncentral_chisquare,
          2,
          2,
          size=sz,
          chunks=3,
      )
      rnd_test(
          generator_class().noncentral_f, 2, 2, 3, size=sz, chunks=3
      )
      rnd_test(generator_class().normal, 2, 2, size=sz, chunks=3)
      rnd_test(generator_class().pareto, 1, size=sz, chunks=3)
      rnd_test(generator_class().poisson, size=sz, chunks=3)
      rnd_test(generator_class().power, 1, size=sz, chunks=3)
      rnd_test(generator_class().rayleigh, size=sz, chunks=3)
      rnd_test(generator_class().triangular, 1, 2, 3, size=sz, chunks=3)
      rnd_test(generator_class().uniform, size=sz, chunks=3)
      rnd_test(generator_class().vonmises, 2, 3, size=sz, chunks=3)
      rnd_test(generator_class().wald, 1, 2, size=sz, chunks=3)
      rnd_test(generator_class().weibull, 2, size=sz, chunks=3)
      rnd_test(generator_class().zipf, 2, size=sz, chunks=3)
      rnd_test(generator_class().standard_cauchy, size=sz, chunks=3)
      rnd_test(
          generator_class().standard_exponential, size=sz, chunks=3
      )
      rnd_test(generator_class().standard_gamma, 2, size=sz, chunks=3)
      rnd_test(generator_class().standard_normal, size=sz, chunks=3)
      rnd_test(generator_class().standard_t, 2, size=sz, chunks=3)
  
imports: from __future__ import annotations
  import pytest
  import numpy
  import dask
  import dask.array
  from dask.array.core import Array
  from dask.array.utils import assert_eq
  from dask.multiprocessing import _dumps, _loads
  from dask.utils import key_split
label_desc: Use image node_id 21 for calling a global function with example usage: test_random_all_with_class_methods(generator_class, sz) without return types