class_docstr: Map between a `list` of stats to `dict` of stats.
class_docstr_tok: ["Map","between","a","`","list","`","of","stats","to","`","dict","`","of","stats","."]
code: def __init__(
      self, sampler_stats_dtypes: Sequence[Mapping[str, type]]
  ) -> None:
      # Keep a list of flat vs. original stat names
      stat_groups = []
      for s, names_dtypes in enumerate(sampler_stats_dtypes):
          group = []
          for statname, dtype in names_dtypes.items():
              flatname = flat_statname(s, statname)
              is_obj = np.dtype(dtype) == np.dtype(object)
              group.append((flatname, statname, is_obj))
          stat_groups.append(group)
      self._stat_groups: List[List[Tuple[str, str, bool]]] = stat_groups
      self.object_stats = {
          fname: (s, sname)
          for s, group in enumerate(self._stat_groups)
          for fname, sname, is_obj in group
          if is_obj
      }
  
imports: import warnings
  from abc import ABC, abstractmethod
  from enum import IntEnum, unique
  from typing import Any, Dict, Iterable, List, Mapping, Sequence, Tuple, Union
  import numpy
  from pytensor.graph.basic import Variable
  from pymc.blocking import PointType, StatDtype, StatsDict, StatShape, StatsType
  from pymc.model import modelcontext
label_desc: Use image node_id 1 to create a new StatsBijection object  with example: obj = StatsBijection(sampler_stats_dtypes)