code: def sample_posterior_predictive_w(
      traces,
      samples: Optional[int] = None,
      models: Optional[List[Model]] = None,
      weights: Optional[ArrayLike] = None,
      random_seed: RandomState = None,
      progressbar: bool = True,
      return_inferencedata: bool = True,
      idata_kwargs: dict = None,
  ):
      """Generate weighted posterior predictive samples from a list of models and
      a list of traces according to a set of weights.
  
      Parameters
      ----------
      traces : list or list of lists
          List of traces generated from MCMC sampling (xarray.Dataset, arviz.InferenceData, or
          MultiTrace), or a list of list containing dicts from find_MAP() or points. The number of
          traces should be equal to the number of weights.
      samples : int, optional
          Number of posterior predictive samples to generate. Defaults to the
          length of the shorter trace in traces.
      models : list of Model
          List of models used to generate the list of traces. The number of models should be equal to
          the number of weights and the number of observed RVs should be the same for all models.
          By default a single model will be inferred from ``with`` context, in this case results will
          only be meaningful if all models share the same distributions for the observed RVs.
      weights : array-like, optional
          Individual weights for each trace. Default, same weight for each model.
      random_seed : int, RandomState or Generator, optional
          Seed for the random number generator.
      progressbar : bool, optional default True
          Whether or not to display a progress bar in the command line. The bar shows the percentage
          of completion, the sampling speed in samples per second (SPS), and the estimated remaining
          time until completion ("expected time of arrival"; ETA).
      return_inferencedata : bool
          Whether to return an :class:`arviz:arviz.InferenceData` (True) object or a dictionary (False).
          Defaults to True.
      idata_kwargs : dict, optional
          Keyword arguments for :func:`pymc.to_inference_data`
  
      Returns
      -------
      arviz.InferenceData or Dict
          An ArviZ ``InferenceData`` object containing the posterior predictive samples from the
          weighted models (default), or a dictionary with variable names as keys, and samples as
          numpy arrays.
      """
      raise FutureWarning(
          "The function `sample_posterior_predictive_w` has been removed in PyMC 4.3.0. "
          "Switch to `arviz.stats.weight_predictions`"
      )
  
imports: import logging
  import warnings
  from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Set, Tuple, Union, cast
  import numpy
  import xarray
  from arviz import InferenceData
  from fastprogress.fastprogress import progress_bar
  from pytensor import tensor
  from pytensor.graph.basic import Apply, Constant, Variable, ancestors, general_toposort, walk
  from pytensor.graph.fg import FunctionGraph
  from pytensor.tensor.random.var import RandomGeneratorSharedVariable, RandomStateSharedVariable
  from pytensor.tensor.sharedvar import SharedVariable
  from typing_extensions import TypeAlias
  import pymc
  from pymc.backends.arviz import _DefaultTrace
  from pymc.backends.base import MultiTrace
  from pymc.blocking import PointType
  from pymc.model import Model, modelcontext
  from pymc.pytensorf import compile_pymc
  from pymc.util import RandomState, _get_seeds_per_chain, dataset_to_point_list, get_default_varnames, point_wrapper
label_desc: Use image node_id 7 for calling a global function with example usage: sample_posterior_predictive_w(traces, samples, models, weights, random_seed, progressbar, return_inferencedata, idata_kwargs) without return types