code: def _std_2poisson_power(
      rate1,
      rate2,
      nobs_ratio=1,
      alpha=0.05,
      exposure=1,
      dispersion=1,
      value=0,
      method_var="score",
  ):
      rates_pooled = (rate1 + rate2 * nobs_ratio) / (1 + nobs_ratio)
      # v1 = dispersion / exposure * (1 / rate2 + 1 / (nobs_ratio * rate1))
      if method_var == "alt":
          v0 = v1 = rate1 + rate2 / nobs_ratio
      else:
          # uaw n1 = 1 as normalization
          _, r1_cmle, r2_cmle = _score_diff(
              rate1,
              1,
              rate2 * nobs_ratio,
              nobs_ratio,
              value=value,
              return_cmle=True,
          )
          v1 = rate1 + rate2 / nobs_ratio
          v0 = r1_cmle + r2_cmle / nobs_ratio
      return rates_pooled, np.sqrt(v0), np.sqrt(v1)
  
imports: import numpy
  import warnings
  from scipy import stats, optimize
  from statsmodels.stats.base import HolderTuple
  from statsmodels.stats.weightstats import _zstat_generic2
  from statsmodels.stats._inference_tools import _mover_confint
label_desc: Use image node_id 17 for calling a global function with example usage: _std_2poisson_power(rate1, rate2, nobs_ratio, alpha, exposure, dispersion, value, method_var) and returns: rates_pooled,  np,  np