class_docstr: Normalizes observations to value range [0, 1].
  
  The strategy subtracts in a first step the minimum observed value to shift the lowest value after
  normalization to zero. In a subsequent step we divide the observation with the maximum of the previous step yielding
  observations in the range [0, 1].
docstr: Implementation of
  :class:`~maze.core.wrappers.observation_normalization.normalization_strategies.base.ObservationNormalizationStrategy`
  interface.
docstr_tok: ["Implementation","of",":","class",":","`","~maze.core.wrappers.observation_normalization.normalization_strategies.base.ObservationNormalizationStrategy","`","interface","."]
code: def normalize_value(self, value: np.ndarray) -> np.ndarray:
      """Implementation of
      :class:`~maze.core.wrappers.observation_normalization.normalization_strategies.base.ObservationNormalizationStrategy`
      interface.
      """
      return (value - self._statistics["min"]) / (
          self._statistics["max"] - self._statistics["min"]
      )
  
file_path: maze/maze/core/wrappers/observation_normalization/normalization_strategies/range_zero_one.py
imports: from typing import List
  import numpy
  from maze.core.annotations import override
  from maze.core.wrappers.observation_normalization.normalization_strategies.base import ObservationNormalizationStrategy, StatisticsType
label_desc: Use image node_id 2 for calling the RangeZeroOneObservationNormalizationStrategy obj's underlying member method code with example usage: obj.normalize_value(value) and returns: unknown