class_docstr: RendezvousInfo is machine identity information that is:
   - configured in the container by the rendezvous layer (when on a Determined cluster)
   - independent of the launch layer
   - consumed by the launch layer
class_docstr_tok: ["RendezvousInfo","is","machine","identity","information","that","is",":","-","configured","in","the","container","by","the","rendezvous","layer","(","when","on","a","Determined","cluster",")","-","independent","of","the","launch","layer","-","consumed","by","the","launch","layer"]
docstr: to_file writes the RendezvousInfo to a well-known location in a Determined container.  This
  is called automatically early in the lifetime of a container, so user code can always expect
  this file to be written before user code runs.
docstr_tok: ["to_file","writes","the","RendezvousInfo","to","a","well-known","location","in","a","Determined","container",".","This","is","called","automatically","early","in","the","lifetime","of","a","container",",","so","user","code","can","always","expect","this","file","to","be","written","before","user","code","runs","."]
code: def _to_file(self, path: str = DEFAULT_RENDEZVOUS_INFO_PATH) -> None:
      """
      to_file writes the RendezvousInfo to a well-known location in a Determined container.  This
      is called automatically early in the lifetime of a container, so user code can always expect
      this file to be written before user code runs.
      """
      with open(path, "w") as f:
          json.dump(vars(self), f)
  
imports: import json
  import os
  from typing import Any, Dict, Iterable, List, Optional, Union
  from determined import gpu
label_desc: Use image node_id 2 for calling the RendezvousInfo obj's underlying member method code with example usage: obj._to_file(path) without return types