class_docstr: A manager of static schedules for a static chain.
  
  This is a container of the static schedules that are used by a static
  chain.
  
  Args:
      minimize_cache_size (bool): If `True`, attempt to reduce memory
      usage by clearing the cached schedules whenever the training
      mode changes (that is, whenever `chainer.config.train` changes
      value) or whenever the mini-batch size changes.
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code: def __init__(self, minimize_cache_size=True, verbosity_level=0):
      # Maps a key string to a list of schedule functions.
      self.schedules = dict()
      self.minimize_cache_size = minimize_cache_size
      self.in_use_count = dict()
      self.forward_over = False
      self.prev_train_config = None
      self.max_in_use_train = 0
      self.train_count = 0
      self.verbosity_level = verbosity_level
  
imports: import sys
  import weakref
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.function_node
label_desc: Use image node_id 1 to create a new ScheduleManager object from inherited base classes: object with example: obj = ScheduleManager(minimize_cache_size, verbosity_level)