class_docstr: Contains the compute environment. If not passed, will try to detect.
  
  Args:
      dist_env: The distributed environment (distributed worldsize and global rank)
      worker_env: The worker environment (number of workers, worker rank)
class_docstr_tok: ["Contains","the","compute","environment",".","If","not","passed",",","will","try","to","detect",".","Args",":","dist_env",":","The","distributed","environment","(","distributed","worldsize","and","global","rank",")","worker_env",":","The","worker","environment","(","number","of","workers",",","worker","rank",")"]
code: def __repr__(self) -> str:
      dist_env_repr = repr(self.dist_env)
      worker_env_repr = repr(self.worker_env)
  
      return (
          f"{self.__class__.__name__}(\n\tdist_env: {dist_env_repr},\n\tworker_env: "
          + f"{worker_env_repr}\n\tnum_shards: {self.num_shards},\n\tshard_rank: {self.shard_rank})"
      )
  
imports: from typing import Callable, Optional
  import torch
  from torch.utils.data import get_worker_info
label_desc: Use image node_id 5 for calling the Environment obj's underlying member method code with example usage: obj.__repr__() and returns: unknown