class_docstr: Wrap Gym environment in TestEnv to save the MiniGrid's
  observation, action, reward and terminal in a list so that
  we can check if replay buffer is working correctly
class_docstr_tok: ["Wrap","Gym","environment","in","TestEnv","to","save","the","MiniGrid's","observation",",","action",",","reward","and","terminal","in","a","list","so","that","we","can","check","if","replay","buffer","is","working","correctly"]
code: def __init__(self, env):
      self.env = env
      self.action_space = self.env.action_space
      # mdp_id, sequence_number, state, action, reward, terminal
      self.sart = []
      self.mdp_id = -1
      self.sequence_number = 0
  
file_path: ReAgent/reagent/gym/tests/test_gym_replay_buffer.py
imports: import logging
  import numpy.testing
  from reagent.core.parameters import ProblemDomain
  from reagent.gym.envs import Gym
  from reagent.gym.envs.wrappers.simple_minigrid import SimpleObsWrapper
  from reagent.gym.utils import create_df_from_replay_buffer
  from reagent.preprocessing.sparse_to_dense import PythonSparseToDenseProcessor
  from reagent.test.base.horizon_test_base import HorizonTestBase
label_desc: Use image node_id 1 to create a new TestEnv object from inherited base classes: SimpleObsWrapper with example: obj = TestEnv(env)