code: def strip(var, n_envs, n_steps, flat=False):
      """
      Removes the last step in the batch
  
      :param var: (TensorFlow Tensor) The input Tensor
      :param n_envs: (int) The number of environments
      :param n_steps: (int) The number of steps to run for each environment
      :param flat: (bool) If the input Tensor is flat
      :return: (TensorFlow Tensor) the input tensor, without the last step in the batch
      """
      out_vars = batch_to_seq(var, n_envs, n_steps + 1, flat)
      return seq_to_batch(out_vars[:-1], flat)
  
imports: import time
  import warnings
  import numpy
  import tensorflow
  from gym.spaces import Discrete, Box
  from collections import deque
  from stable_baselines import logger
  from stable_baselines.common.schedules import Scheduler
  from stable_baselines.common.tf_util import batch_to_seq, seq_to_batch, check_shape, avg_norm, gradient_add, q_explained_variance, total_episode_reward_logger
  from stable_baselines.acer.buffer import Buffer
  from stable_baselines.common import ActorCriticRLModel, tf_util, SetVerbosity, TensorboardWriter
  from stable_baselines.common.runners import AbstractEnvRunner
  from stable_baselines.common.policies import ActorCriticPolicy, RecurrentActorCriticPolicy
label_desc: Use image node_id 2 for calling a global function with example usage: strip(var, n_envs, n_steps, flat) and returns: seq_to_batch