code: def global_init():
      """Modifies the global environment for running TensorBoard as main.
  
      This functions changes global state in the Python process, so it should
      not be called from library routines.
      """
      # TF versions prior to 1.15.0 included default GCS filesystem caching logic
      # that interacted pathologically with the pattern of reads used by TensorBoard
      # for logdirs. See: https://github.com/tensorflow/tensorboard/issues/1225
      # The problematic behavior was fixed in 1.15.0 by
      # https://github.com/tensorflow/tensorflow/commit/e43b94649d3e1ac5d538e4eca9166b899511d681
      # but for older versions of TF, we avoid a regression by setting this env var to
      # disable the cache, which must be done before the first import of tensorflow.
      os.environ["GCS_READ_CACHE_DISABLED"] = "1"
  
      if getattr(tf, "__version__", "stub") == "stub":
          print(
              "TensorFlow installation not found - running with reduced feature set.",
              file=sys.stderr,
          )
  
      # Only emit log messages at WARNING and above by default to reduce spam.
      absl.logging.set_verbosity(absl.logging.WARNING)
  
      # Intercept attempts to invoke `tensorboard dev` and print turndown message.
      if sys.argv[1:] and sys.argv[1] == "dev":
          sys.stderr.write(_TBDEV_SHUTDOWN_MESSAGE)
          sys.stderr.flush()
          sys.exit(1)
  
imports: import os
  import sys
  import absl.logging
  from tensorboard.compat import tf
label_desc: Use image node_id 1 for calling a global function with example usage: global_init() without return types