class_docstr: Mixin for TorchPolicy that adds a learning rate schedule.
class_docstr_tok: ["Mixin","for","TorchPolicy","that","adds","a","learning","rate","schedule","."]
code: def __init__(self, lr, lr_schedule):
      self._lr_schedule = None
      # Disable any scheduling behavior related to learning if Learner API is active.
      # Schedules are handled by Learner class.
      if lr_schedule is None:
          self.cur_lr = lr
      else:
          self._lr_schedule = PiecewiseSchedule(
              lr_schedule,
              outside_value=lr_schedule[-1][-1],
              framework=None,
          )
          self.cur_lr = self._lr_schedule.value(0)
  
imports: from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_policy import TorchPolicy
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.schedules import PiecewiseSchedule
label_desc: Use image node_id 1 to create a new LearningRateSchedule object  with example: obj = LearningRateSchedule(lr, lr_schedule)