class_docstr: Hook that will set a linearly interpolated value.
  
  You can use this hook to decay the learning rate by using a setter function
  as follows:
  
  .. code-block:: python
  
      def lr_setter(env, agent, value):
          agent.optimizer.lr = value
  
      hook = LinearInterpolationHook(10 ** 6, 1e-3, 0, lr_setter)
  
  
  Args:
      total_steps (int): Number of total steps.
      start_value (float): Start value.
      stop_value (float): Stop value.
      setter (callable): (env, agent, value) -> None
code: def __call__(self, env, agent, step):
      value = np.interp(
          step,
          [1, self.total_steps],
          [self.start_value, self.stop_value],
      )
      self.setter(env, agent, value)
  
file_path: chainerrl/chainerrl/experiments/hooks.py
imports: from abc import ABCMeta
  from abc import abstractmethod
  import numpy
label_desc: Use image node_id 2 for calling the LinearInterpolationHook obj's underlying member method code with example usage: obj.__call__(env, agent, step) without return types