code: def _log_gampoiss(k, alpha, beta):
      import math
  
      k = int(k)
      return (
          math.lgamma(k + alpha)
          + alpha * np.log(beta)
          - math.lgamma(alpha)
          - math.lgamma(k + 1)
          - (alpha + k) * np.log(1 + beta)
      )
  
imports: import itertools
  import operator
  import os
  import warnings
  from os.path import join
  from bisect import bisect_left
  from collections import defaultdict
  from copy import deepcopy
  from itertools import combinations
  from random import sample
  import numpy
  import pandas
  from mlxtend.frequent_patterns import fpgrowth
  from numpy.random import random
  from pandas import read_csv
  from scipy.sparse import csc_matrix
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_X_y, check_is_fitted
  from imodels.rule_set.rule_set import RuleSet
  from imodels.util.arguments import check_fit_arguments
label_desc: Use image node_id 3 for calling a global function with example usage: _log_gampoiss(k, alpha, beta) and returns: unknown