code: def feature_transform(
      features,
      single_elem_transform,
      is_next_with_multi_steps=False,
      replace_when_terminal=None,
      terminal=None,
  ):
      """feature_transform is a method on a single row.
      We assume features is List[features] (batch of features).
      This can also be called for next_features with multi_steps which we assume
      to be List[List[features]]. First List is denoting that it's a batch,
      second List is denoting that a single row consists of a list of features.
      """
      if is_next_with_multi_steps:
          if terminal is None:
              return [
                  [
                      single_elem_transform(feat)
                      for feat in multi_steps_features
                  ]
                  for multi_steps_features in features
              ]
          else:
              # for next features where we replace them when terminal
              assert replace_when_terminal is not None
              return [
                  [
                      single_elem_transform(feat)
                      for feat in multi_steps_features
                  ]
                  if not terminal[idx]
                  else [
                      single_elem_transform(feat)
                      for feat in multi_steps_features[:-1]
                  ]
                  + [replace_when_terminal]
                  for idx, multi_steps_features in enumerate(features)
              ]
      else:
          if terminal is None:
              return [single_elem_transform(feat) for feat in features]
          else:
              assert replace_when_terminal is not None
              return [
                  single_elem_transform(feat)
                  if not terminal[idx]
                  else replace_when_terminal
                  for idx, feat in enumerate(features)
              ]
  
imports: import logging
  import random
  from typing import Dict, List, Optional
  import gym
  import numpy
  import pandas
  import torch
  import torch.nn.functional
  from gym import spaces
  from reagent.core.parameters import NormalizationData, NormalizationKey, ProblemDomain
  from reagent.gym.agents.agent import Agent
  from reagent.gym.agents.post_step import add_replay_buffer_post_step
  from reagent.gym.envs import EnvWrapper
  from reagent.gym.normalizers import discrete_action_normalizer, only_continuous_action_normalizer, only_continuous_normalizer
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.runners.gymrunner import run_episode
  from reagent.replay_memory import ReplayBuffer
  from tqdm import tqdm
label_desc: Use image node_id 7 for calling a global function with example usage: feature_transform(features, single_elem_transform, is_next_with_multi_steps, replace_when_terminal, terminal) and returns: unknown, unknown, unknown, unknown