class_docstr: Statistic tracking the count, mean, and (diagonal) variance of a single
  :class:`torch.Tensor`.
docstr: :returns: A dictionary with keys ``count: int`` and (if any samples
      have been collected) ``mean: torch.Tensor`` and ``variance:
      torch.Tensor``.
  :rtype: dict
docstr_tok: [":","returns",":","A","dictionary","with","keys","``","count",":","int","``","and","(","if","any","samples","have","been","collected",")","``","mean",":","torch.Tensor","``","and","``","variance",":","torch.Tensor","``",".",":","rtype",":","dict"]
code: def get(self) -> Dict[str, Union[int, torch.Tensor]]:
      """
      :returns: A dictionary with keys ``count: int`` and (if any samples
          have been collected) ``mean: torch.Tensor`` and ``variance:
          torch.Tensor``.
      :rtype: dict
      """
      if self.shape is None:
          return {"count": 0}
      count = self.welford.n_samples
      mean = self.welford._mean.reshape(self.shape)
      variance = self.welford.get_covariance(regularize=False).reshape(
          self.shape
      )
      return {"count": count, "mean": mean, "variance": variance}
  
imports: import copy
  from abc import ABC, abstractmethod
  from collections import defaultdict
  from typing import Any, Callable, Dict, Hashable, Union
  import torch
  from pyro.ops.welford import WelfordCovariance
label_desc: Use image node_id 4 for calling the CountMeanVarianceStats obj's underlying member method code with example usage: obj.get() and returns: dict, dict