class_docstr: Observation normalizer.
class_docstr_tok: ["Observation","normalizer","."]
docstr: Initialization.
  
  Args:
      obs_shape - Shape of the observations.
      std_min_value - Minimum standard deviation.
      std_max_value - Maximum standard deviation.
      dummy - Whether this is a dummy normalizer.
docstr_tok: ["Initialization",".","Args",":","obs_shape","-","Shape","of","the","observations",".","std_min_value","-","Minimum","standard","deviation",".","std_max_value","-","Maximum","standard","deviation",".","dummy","-","Whether","this","is","a","dummy","normalizer","."]
code: def __init__(
      self,
      obs_shape: Tuple,
      clip_value: float = 5.0,
      std_min_value: float = 1e-6,
      std_max_value: float = 1e6,
      dummy: bool = False,
  ):
      """Initialization.
  
      Args:
          obs_shape - Shape of the observations.
          std_min_value - Minimum standard deviation.
          std_max_value - Maximum standard deviation.
          dummy - Whether this is a dummy normalizer.
      """
  
      self._obs_shape = obs_shape
      self._obs_size = np.prod(obs_shape)
      self._std_min_value = std_min_value
      self._std_max_value = std_max_value
      self._clip_value = clip_value
      self.is_dummy = dummy
  
file_path: evojax/evojax/obs_norm.py
imports: import numpy
  from typing import Tuple
  from functools import partial
  import jax
  import jax.numpy
label_desc: Use image node_id 1 to create a new ObsNormalizer object from inherited base classes: object with example: obj = ObsNormalizer(obs_shape, clip_value, std_min_value, std_max_value, dummy)