code: def predictions_to_inference_data(
      predictions,
      posterior_trace: Optional["MultiTrace"] = None,
      model: Optional["Model"] = None,
      coords: Optional[CoordSpec] = None,
      dims: Optional[DimSpec] = None,
      sample_dims: Optional[List] = None,
      idata_orig: Optional[InferenceData] = None,
      inplace: bool = False,
  ) -> InferenceData:
      """Translate out-of-sample predictions into ``InferenceData``.
  
      Parameters
      ----------
      predictions: Dict[str, np.ndarray]
          The predictions are the return value of :func:`~pymc.sample_posterior_predictive`,
          a dictionary of strings (variable names) to numpy ndarrays (draws).
          Requires the arrays to follow the convention ``chain, draw, *shape``.
      posterior_trace: MultiTrace
          This should be a trace that has been thinned appropriately for
          ``pymc.sample_posterior_predictive``. Specifically, any variable whose shape is
          a deterministic function of the shape of any predictor (explanatory, independent, etc.)
          variables must be *removed* from this trace.
      model: Model
          The pymc model. It can be omitted if within a model context.
      coords: Dict[str, array-like[Any]]
          Coordinates for the variables.  Map from coordinate names to coordinate values.
      dims: Dict[str, array-like[str]]
          Map from variable name to ordered set of coordinate names.
      idata_orig: InferenceData, optional
          If supplied, then modify this inference data in place, adding ``predictions`` and
          (if available) ``predictions_constant_data`` groups. If this is not supplied, make a
          fresh InferenceData
      inplace: boolean, optional
          If idata_orig is supplied and inplace is True, merge the predictions into idata_orig,
          rather than returning a fresh InferenceData object.
  
      Returns
      -------
      InferenceData:
          May be modified ``idata_orig``.
      """
      if inplace and not idata_orig:
          raise ValueError(
              "Do not pass True for inplace unless passing an existing InferenceData as idata_orig"
          )
      converter = InferenceDataConverter(
          trace=posterior_trace,
          predictions=predictions,
          model=model,
          coords=coords,
          dims=dims,
          sample_dims=sample_dims,
          log_likelihood=False,
      )
      if hasattr(idata_orig, "posterior"):
          assert idata_orig is not None
          converter.nchains = idata_orig["posterior"].sizes["chain"]
          converter.ndraws = idata_orig["posterior"].sizes["draw"]
      else:
          aelem = next(iter(predictions.values()))
          converter.nchains, converter.ndraws = aelem.shape[:2]
      new_idata = converter.to_inference_data()
      if idata_orig is None:
          return new_idata
      elif inplace:
          concat([idata_orig, new_idata], dim=None, inplace=True)
          return idata_orig
      else:
          # if we are not returning in place, then merge the old groups into the new inference
          # data and return that.
          concat(
              [new_idata, idata_orig], dim=None, copy=True, inplace=True
          )
          return new_idata
  
imports: import logging
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  import numpy
  from arviz import InferenceData, concat, rcParams
  from arviz.data.base import CoordSpec, DimSpec, dict_to_dataset, requires
  from pytensor.graph.basic import Constant
  from pytensor.tensor.sharedvar import SharedVariable
  import pymc
  from pymc.model import Model, modelcontext
  from pymc.pytensorf import extract_obs_data
  from pymc.util import get_default_varnames
label_desc: Use image node_id 5 for calling a global function with example usage: predictions_to_inference_data(predictions, posterior_trace, model, coords, dims, sample_dims, idata_orig, inplace) and returns: new_idata, idata_orig, new_idata