code: def test_sample_poisson(poisson_lambda):
      """
      We test whether the first two moments match a Poisson distribution
      """
      sample = _sample_poisson(np.ones(shape=10000) * poisson_lambda)
      tol = 0.05
      assert np.isclose(sample.mean(), poisson_lambda, atol=0, rtol=tol)
      assert np.isclose(sample.var(), poisson_lambda, atol=0, rtol=tol)
  
imports: import operator
  from typing import Hashable, Tuple, Union
  import networkx
  import numpy
  import pandas
  import pytest
  from networkx.algorithms.dag import is_directed_acyclic_graph
  from sklearn.gaussian_process.kernels import RBF
  from causalnex.structure.data_generators.core import _sample_binary_from_latent, _sample_count_from_latent, _sample_poisson, generate_structure, nonlinear_sem_generator, sem_generator
  from causalnex.structure.structuremodel import StructureModel
label_desc: Use image node_id 5 for calling a global function with example usage: test_sample_poisson(poisson_lambda) without return types