code: def create_multidimensional_model(seed=10, transpose=False):
      """Create model with fake data."""
      np.random.seed(seed)
      nchains = 4
      ndraws = 500
      ndim1 = 5
      ndim2 = 7
      data = {
          "y": np.random.normal(size=(ndim1, ndim2)),
          "sigma": np.random.normal(size=(ndim1, ndim2)),
      }
      posterior = {
          "mu": np.random.randn(nchains, ndraws),
          "tau": abs(np.random.randn(nchains, ndraws)),
          "eta": np.random.randn(nchains, ndraws, ndim1, ndim2),
          "theta": np.random.randn(nchains, ndraws, ndim1, ndim2),
      }
      posterior_predictive = {
          "y": np.random.randn(nchains, ndraws, ndim1, ndim2)
      }
      sample_stats = {
          "energy": np.random.randn(nchains, ndraws),
          "diverging": np.random.randn(nchains, ndraws) > 0.90,
      }
      log_likelihood = {
          "y": np.random.randn(nchains, ndraws, ndim1, ndim2),
      }
      prior = {
          "mu": np.random.randn(nchains, ndraws) / 2,
          "tau": abs(np.random.randn(nchains, ndraws)) / 2,
          "eta": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2,
          "theta": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2,
      }
      prior_predictive = {
          "y": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2
      }
      sample_stats_prior = {
          "energy": np.random.randn(nchains, ndraws),
          "diverging": (np.random.randn(nchains, ndraws) > 0.95).astype(
              int
          ),
      }
      model = from_dict(
          posterior=posterior,
          posterior_predictive=posterior_predictive,
          sample_stats=sample_stats,
          log_likelihood=log_likelihood,
          prior=prior,
          prior_predictive=prior_predictive,
          sample_stats_prior=sample_stats_prior,
          observed_data={"y": data["y"]},
          dims={
              "y": ["dim1", "dim2"],
              "log_likelihood": ["dim1", "dim2"],
          },
          coords={"dim1": range(ndim1), "dim2": range(ndim2)},
      )
      if transpose:
          for group in model._groups:
              group_dataset = getattr(model, group)
              if all(
                  dim in group_dataset.dims for dim in ("draw", "chain")
              ):
                  setattr(
                      model,
                      group,
                      group_dataset.transpose(*["draw", "chain"], ...),
                  )
      return model
  
imports: import gzip
  import importlib
  import logging
  import os
  import sys
  from typing import Any, Dict, List, Optional, Tuple, Union
  import cloudpickle
  import numpy
  import pytest
  from _pytest.outcomes import Skipped
  from packaging.version import Version
  from ..data import InferenceData, from_dict
label_desc: Use image node_id 5 for calling a global function with example usage: create_multidimensional_model(seed, transpose) and returns: model