class_docstr: Insufficient resources manager.
  
  Makes best effort, conservative guesses about if Tune loop is stuck due to
  infeasible resources. If so, outputs usability messages for users to
  act upon.
code: def __init__(self, for_train: bool = False):
      # The information tracked across the life time of Tune loop.
      self._no_running_trials_since = -1
      self._last_trial_num = -1
      self._for_train = for_train
  
file_path: ray/python/ray/tune/execution/insufficient_resources_manager.py
imports: import logging
  from functools import lru_cache
  import os
  import ray
  import time
  from typing import Dict, Optional, Tuple
  from ray.tune.execution.cluster_info import _is_ray_cluster
  from ray.tune.experiment import Trial
label_desc: Use image node_id 1 to create a new _InsufficientResourcesManager object  with example: obj = _InsufficientResourcesManager(for_train)