class_docstr: Stores the mean and bounds (uncertainty interval) of 'comparisons' and
  'slopes' estimates. Used in 'PredictiveDifferences' to store typed data
  for the summary dataframe.
  
  Parameters
  ----------
  mean : Dict[str, xr.DataArray]
      The mean of the posterior distribution (chains and draws).
  bounds : Dict[str, xr.Dataset]
      The uncertainty interval of the posterior distribution (chains and draws).
  use_hdi : bool
      Whether to use the highest density interval (HDI) (True) or quantiles (False).
docstr: Parses the mean and bounds dictionaries into arrays for inserting
  the 'mean', 'lower', and 'upper' columns into the summary dataframe.
docstr_tok: ["Parses","the","mean","and","bounds","dictionaries","into","arrays","for","inserting","the","'mean","'",",","'lower","'",",","and","'upper","'","columns","into","the","summary","dataframe","."]
code: def __post_init__(self):
      """
      Parses the mean and bounds dictionaries into arrays for inserting
      the 'mean', 'lower', and 'upper' columns into the summary dataframe.
      """
      self.bounds_list = [self.bounds[key] for key in self.bounds]
      self.mean = np.array(list(self.mean.values())).flatten()
  
      if self.use_hdi:
          data_var = list(self.bounds_list[0].data_vars)[0]
          self.lower = np.array(
              [
                  self.bounds[key][data_var].sel(hdi="lower")
                  for key in self.bounds
              ]
          ).flatten()
          self.higher = np.array(
              [
                  self.bounds[key][data_var].sel(hdi="higher")
                  for key in self.bounds
              ]
          ).flatten()
      else:
          lower = self.bounds_list[0].coords["quantile"].values[0]
          higher = self.bounds_list[0].coords["quantile"].values[1]
          self.lower = np.array(
              [
                  self.bounds[key].sel(quantile=lower)
                  for key in self.bounds
              ]
          ).flatten()
          self.higher = np.array(
              [
                  self.bounds[key].sel(quantile=higher)
                  for key in self.bounds
              ]
          ).flatten()
  
imports: from dataclasses import dataclass, field
  import itertools
  from typing import Dict, Union
  import arviz
  import numpy
  import pandas
  from pandas.api.types import is_categorical_dtype, is_string_dtype
  import xarray
  from bambi.models import Model
  from bambi.interpret.create_data import create_differences_data, create_predictions_data
  from bambi.interpret.utils import average_over, ConditionalInfo, enforce_dtypes, identity, merge, VariableInfo
  from bambi.utils import get_aliased_name
label_desc: Use image node_id 1 for calling the Estimate obj's underlying member method code with example usage: obj.__post_init__() without return types