code: def create_for_env(cls, env: gym.Env):
      action_space = env.action_space
      assert isinstance(action_space, gym.spaces.Discrete)
      try:
          return cls(
              num_actions=action_space.n,
              # pyre-fixme[16]: `Env` has no attribute `trainer_preprocessor`.
              trainer_preprocessor=env.trainer_preprocessor,
          )
      except AttributeError:
          return cls(num_actions=action_space.n)
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 2 for calling the DiscreteDqnInputMaker obj's underlying member method code with example usage: obj.create_for_env(cls, env) and returns: cls, cls