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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docstr: Returns the value based on a time step input.
  
  Args:
      t: The time step. This could be a tf.Tensor.
  
  Returns:
      The calculated value depending on the schedule and `t`.
docstr_tok: ["Returns","the","value","based","on","a","time","step","input",".","Args",":","t",":","The","time","step",".","This","could","be","a","tf.Tensor",".","Returns",":","The","calculated","value","depending","on","the","schedule","and","`","t","`","."]
code: def _value(self, t: Union[int, TensorType]) -> Any:
      """
      Returns the value based on a time step input.
  
      Args:
          t: The time step. This could be a tf.Tensor.
  
      Returns:
          The calculated value depending on the schedule and `t`.
      """
      raise NotImplementedError
  
file_path: 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 4 for calling the Schedule obj's underlying member method code with example usage: obj._value(t) without return types