class_docstr: Base trace object
  
  Parameters
  ----------
  name: str
      Name of backend
  model: Model
      If None, the model is taken from the `with` context.
  vars: list of variables
      Sampling values will be stored for these variables. If None,
      `model.unobserved_RVs` is used.
  test_point: dict
      use different test point that might be with changed variables shapes
class_docstr_tok: ["Base","trace","object","Parameters","--","--","--","--","--","name",":","str","Name","of","backend","model",":","Model","If","None",",","the","model","is","taken","from","the","`","with","`","context",".","vars",":","list","of","variables","Sampling","values","will","be","stored","for","these","variables",".","If","None",",","`","model.unobserved_RVs","`","is","used",".","test_point",":","dict","use","different","test","point","that","might","be","with","changed","variables","shapes"]
docstr: Get sampler statistics.
docstr_tok: ["Get","sampler","statistics","."]
code: def _get_sampler_stats(
      self, stat_name: str, sampler_idx: int, burn: int, thin: int
  ) -> np.ndarray:
      """Get sampler statistics."""
      raise NotImplementedError()
  
imports: import itertools
  import logging
  import warnings
  from abc import ABC
  from typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Sized, Tuple, TypeVar, Union, cast
  import numpy
  from pymc.backends.report import SamplerReport
  from pymc.model import modelcontext
  from pymc.util import get_var_name
label_desc: Use image node_id 6 for calling the BaseTrace obj's underlying member method code with example usage: obj._get_sampler_stats(stat_name, sampler_idx, burn, thin) without return types