code: def weight_predictions(idatas, weights=None):
      """
      Generate weighted posterior predictive samples from a list of InferenceData
      and a set of weights.
  
      Parameters
      ---------
      idatas : list[InferenceData]
          List of :class:`arviz.InferenceData` objects containing the groups `posterior_predictive`
          and `observed_data`. Observations should be the same for all InferenceData objects.
      weights : array-like, optional
          Individual weights for each model. Weights should be positive. If they do not sum up to 1,
          they will be normalized. Default, same weight for each model.
          Weights can be computed using many different methods including those in
          :func:`arviz.compare`.
  
      Returns
      -------
      idata: InferenceData
          Output InferenceData object with the groups `posterior_predictive` and `observed_data`.
  
      See Also
      --------
      compare :  Compare models based on PSIS-LOO `loo` or WAIC `waic` cross-validation
      """
      if len(idatas) < 2:
          raise ValueError(
              "You should provide a list with at least two InferenceData objects"
          )
  
      if not all(
          "posterior_predictive" in idata.groups() for idata in idatas
      ):
          raise ValueError(
              "All the InferenceData objects must contain the `posterior_predictive` group"
          )
  
      if not all(
          idatas[0].observed_data.equals(idata.observed_data)
          for idata in idatas[1:]
      ):
          raise ValueError(
              "The observed data should be the same for all InferenceData objects"
          )
  
      if weights is None:
          weights = np.ones(len(idatas)) / len(idatas)
      elif len(idatas) != len(weights):
          raise ValueError(
              "The number of weights should be the same as the number of InferenceData objects"
          )
  
      weights = np.array(weights, dtype=float)
      weights /= weights.sum()
  
      len_idatas = [
          idata.posterior_predictive.dims["chain"]
          * idata.posterior_predictive.dims["draw"]
          for idata in idatas
      ]
  
      if not all(len_idatas):
          raise ValueError("At least one of your idatas has 0 samples")
  
      new_samples = (np.min(len_idatas) * weights).astype(int)
  
      new_idatas = [
          extract(
              idata, group="posterior_predictive", num_samples=samples
          ).reset_coords()
          for samples, idata in zip(new_samples, idatas)
      ]
  
      weighted_samples = InferenceData(
          posterior_predictive=xr.concat(new_idatas, dim="sample"),
          observed_data=idatas[0].observed_data,
      )
  
      return weighted_samples
  
imports: import itertools
  import warnings
  from copy import deepcopy
  from typing import List, Optional, Tuple, Union, Mapping, cast, Callable
  import numpy
  import pandas
  import scipy.stats
  from xarray_einstats import stats
  import xarray
  from scipy.optimize import minimize
  from typing_extensions import Literal
  from ..None import _log
  from ..data import InferenceData, convert_to_dataset, convert_to_inference_data, extract
  from ..rcparams import rcParams, ScaleKeyword, ICKeyword
  from ..utils import Numba, _numba_var, _var_names, get_coords
  from .density_utils import get_bins
  from .density_utils import histogram
  from .density_utils import kde
  from .diagnostics import _mc_error, _multichain_statistics, ess
  from .stats_utils import ELPDData, _circular_standard_deviation, smooth_data
  from .stats_utils import get_log_likelihood
  from .stats_utils import get_log_prior
  from .stats_utils import logsumexp
  from .stats_utils import make_ufunc
  from .stats_utils import stats_variance_2d
  from .stats_utils import wrap_xarray_ufunc
  from ..sel_utils import xarray_var_iter
  from ..labels import BaseLabeller
label_desc: Use image node_id 19 for calling a global function with example usage: weight_predictions(idatas, weights) and returns: weighted_samples