class_docstr: Encapsulate InferenceData specific logic.
docstr: Convert constant data to xarray.
docstr_tok: ["Convert","constant","data","to","xarray","."]
code: def constant_data_to_xarray(self):
      """Convert constant data to xarray."""
      constant_data = find_constants(self.model)
      if not constant_data:
          return None
  
      xarray_dataset = dict_to_dataset(
          constant_data,
          library=pymc,
          coords=self.coords,
          dims=self.dims,
          default_dims=[],
      )
  
      # provisional handling of scalars in constant
      # data to prevent promotion to rank 1
      # in the future this will be handled by arviz
      scalars = [
          var_name
          for var_name, value in constant_data.items()
          if np.ndim(value) == 0
      ]
      for s in scalars:
          s_dim_0_name = f"{s}_dim_0"
          xarray_dataset = xarray_dataset.squeeze(
              s_dim_0_name, drop=True
          )
  
      return xarray_dataset
  
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 9 for calling the InferenceDataConverter obj's underlying member method code with example usage: obj.constant_data_to_xarray() and returns: xarray_dataset, None