class_docstr: Exponential decay schedule from `initial_p` to `final_p`.
  
  Reduces output over `schedule_timesteps`. After this many time steps
  always returns `final_p`.
docstr: Returns the result of: initial_p * decay_rate ** (`t`/t_max).
docstr_tok: ["Returns","the","result","of",":","initial_p","*","decay_rate","*","*","(","`","t","`","\/t_max",")","."]
code: def _value(self, t: TensorType) -> TensorType:
      """Returns the result of: initial_p * decay_rate ** (`t`/t_max)."""
      if (
          self.framework == "torch"
          and torch
          and isinstance(t, torch.Tensor)
      ):
          t = t.float()
      return self.initial_p * self.decay_rate ** (
          t / self.schedule_timesteps
      )
  
file_path: ray/rllib/utils/schedules/exponential_schedule.py
imports: from typing import Optional
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.schedules.schedule import Schedule
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 2 for calling the ExponentialSchedule obj's underlying member method code with example usage: obj._value(t) and returns: unknown