class_docstr: Schedule classes implement various time-dependent scheduling schemas.
  
  - Constant behavior.
  - Linear decay.
  - Piecewise decay.
  - Exponential decay.
  
  Useful for backend-agnostic rate/weight changes for learning rates,
  exploration epsilons, beta parameters for prioritized replay, loss weights
  decay, etc..
  
  Each schedule can be called directly with the `t` (absolute time step)
  value and returns the value dependent on the Schedule and the passed time.
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code: def __init__(self, framework):
      self.framework = framework
  
file_path: ray/python/ray/rllib/utils/schedules/schedule.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Any, Union
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new Schedule object  with example: obj = Schedule(framework)