class_docstr: Store and load tf event logs from gcs.
  
  Authentication is currently only supported via the "Application
  Default Credentials" method in GCP [1]. Typical configuration:
  ensure your VM runs in a service account that has sufficient
  permissions to read/write/delete from the GCS bucket where
  checkpoints will be stored (this only works when running in GCE).
code: def __init__(
      self,
      bucket: str,
      prefix: Optional[str],
      *args: Any,
      **kwargs: Any,
  ) -> None:
      super().__init__(*args, **kwargs)
      import google.cloud.storage
  
      self.client = google.cloud.storage.Client()
      self.bucket = self.client.bucket(bucket)
      self.prefix = normalize_prefix(prefix)
  
file_path: determined/harness/determined/tensorboard/gcs.py
imports: import logging
  import os
  import pathlib
  from typing import Any, List, Optional, no_type_check
  import requests.exceptions
  import urllib3.exceptions
  from determined.common.storage.s3 import normalize_prefix
  from determined.tensorboard import base
label_desc: Use image node_id 1 to create a new GCSTensorboardManager object from inherited base classes: base with example: obj = GCSTensorboardManager(bucket, prefix)