class_docstr: TaskSampler that samples from a finite pool of environments.
  
  This can be used with any environments, but is generally best when using
  in-process samplers with environments that are expensive to construct.
  
  Args:
      envs (list[Environment]): List of environments to use as a pool.
class_docstr_tok: ["TaskSampler","that","samples","from","a","finite","pool","of","environments",".","This","can","be","used","with","any","environments",",","but","is","generally","best","when","using","in-process","samplers","with","environments","that","are","expensive","to","construct",".","Args",":","envs","(","list","[","Environment","]",")",":","List","of","environments","to","use","as","a","pool","."]
code: def __init__(self, envs):
      self._envs = envs
  
imports: import abc
  import copy
  import math
  import numpy
  from garage.envs import GymEnv, TaskNameWrapper, TaskOnehotWrapper
  from garage.sampler.env_update import ExistingEnvUpdate, NewEnvUpdate, SetTaskUpdate
label_desc: Use image node_id 1 to create a new EnvPoolSampler object from inherited base classes: TaskSampler with example: obj = EnvPoolSampler(envs)