class_docstr: Track moving mean, std and count.
class_docstr_tok: ["Track","moving","mean",",","std","and","count","."]
docstr: Normalize input batch using moving mean and std.
  
  Args:
      inputs: Input batch to normalize.
  
  Returns:
      Logarithmic scaled normalized output.
docstr_tok: ["Normalize","input","batch","using","moving","mean","and","std",".","Args",":","inputs",":","Input","batch","to","normalize",".","Returns",":","Logarithmic","scaled","normalized","output","."]
code: def __call__(self, inputs: np.ndarray) -> np.ndarray:
      """Normalize input batch using moving mean and std.
  
      Args:
          inputs: Input batch to normalize.
  
      Returns:
          Logarithmic scaled normalized output.
      """
      batch_mean = np.mean(inputs, axis=0)
      batch_var = np.var(inputs, axis=0)
      batch_count = inputs.shape[0]
      self.update_params(batch_mean, batch_var, batch_count)
      return np.log(inputs / self.std + 1)
  
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 2 for calling the _MovingMeanStd obj's underlying member method code with example usage: obj.__call__(inputs) and returns: np