class_docstr: Track moving mean, std and count.
class_docstr_tok: ["Track","moving","mean",",","std","and","count","."]
docstr: Initialize object.
  
  Args:
      epsilon: Initial count.
      shape: Shape of the trackables mean and std.
docstr_tok: ["Initialize","object",".","Args",":","epsilon",":","Initial","count",".","shape",":","Shape","of","the","trackables","mean","and","std","."]
code: def __init__(
      self, epsilon: float = 1e-4, shape: Optional[List[int]] = None
  ):
      """Initialize object.
  
      Args:
          epsilon: Initial count.
          shape: Shape of the trackables mean and std.
      """
      if not shape:
          shape = []
      self.mean = np.zeros(shape, dtype=np.float32)
      self.var = np.ones(shape, dtype=np.float32)
      self.count = epsilon
  
imports: from gymnasium.spaces import Box, Discrete, Space
  import numpy
  from typing import List, Optional, Union
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.exploration.exploration import Exploration
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import FromConfigSpec, ModelConfigDict, TensorType
label_desc: Use image node_id 1 to create a new _MovingMeanStd object  with example: obj = _MovingMeanStd(epsilon, shape)