class_docstr: Algorithm that returns sigmoid learning curves.
  
  This can be helpful for evaluating early stopping algorithms.
class_docstr_tok: ["Algorithm","that","returns","sigmoid","learning","curves",".","This","can","be","helpful","for","evaluating","early","stopping","algorithms","."]
code: def step(self):
      i = max(0, self.iteration - self.config.offset)
      v = np.tanh(float(i) / self.config.width)
      v *= self.config.height
      return dict(
          episode_reward_mean=v,
          episode_len_mean=v,
          timesteps_this_iter=self.config.iter_timesteps,
          time_this_iter_s=self.config.iter_time,
          info={},
      )
  
imports: import os
  import pickle
  import time
  import numpy
  from ray.tune import result
  from ray.rllib.algorithms.algorithm import Algorithm, AlgorithmConfig
  from ray.rllib.utils.annotations import override
label_desc: Use image node_id 2 for calling the _SigmoidFakeData obj's underlying member method code with example usage: obj.step() and returns: dict