class_docstr: Set hyperparameters by a predefined epoch-based schedule.
code: def _check_value_at_beginning(self):
      v = None
      # we are at `before_train`, therefore the epoch/step associated with `current_point` has finished.
      for p in range(0, self._current_point() + 1):
          v = self._get_value_to_set_at_point(p) or v
      actual_value = self.param.get_value()
      current_point = (
          "step" if self._step else "epoch" + str(self._current_point())
      )
      if v is not None and not np.isclose(v, actual_value):
          logger.warn(
              "According to scheduler {}, parameter '{}' should become {:.7g} at the current point ({}). "
              "However its current value is {:.7g}. ".format(
                  self,
                  self.param.readable_name,
                  v,
                  current_point,
                  actual_value,
              )
          )
          if self._set_at_beginning:
              logger.info(
                  "Setting '{}' to {:.7g}.".format(
                      self.param.readable_name, v
                  )
              )
              self.param.set_value(v)
          else:
              logger.warn(
                  "If there is no other scheduler being used, you may want to check whether your "
                  "initialization of the parameter is as expected"
              )
  
imports: import operator
  import os
  import numpy
  from abc import ABCMeta, abstractmethod
  from collections import deque
  import six
  from ..compat import tfv1
  from ..tfutils.common import get_op_tensor_name
  from ..utils import logger
  from .base import Callback
label_desc: Use image node_id 4 for calling the ScheduledHyperParamSetter obj's underlying member method code with example usage: obj._check_value_at_beginning() without return types