class_docstr: Set hyperparameter by loading the value from a file each time it get called.
  This is useful for manually tuning some parameters (e.g. learning_rate)
  without interrupting the training.
docstr: Args:
      param: same as in :class:`HyperParamSetter`.
      file_name(str): a file containing the new value of the parameter.
          Each line in the file is a ``k:v`` pair, for example, ``learning_rate:1e-4``.
          If the pair is not found, the param will not be changed.
docstr_tok: ["Args",":","param",":","same","as","in",":","class",":","`","HyperParamSetter","`",".","file_name","(","str",")",":","a","file","containing","the","new","value","of","the","parameter",".","Each","line","in","the","file","is","a","``","k",":","v","``","pair",",","for","example",",","``","learning_rate:1e-4","``",".","If","the","pair","is","not","found",",","the","param","will","not","be","changed","."]
code: def __init__(self, param, file_name="hyper.txt"):
      """
      Args:
          param: same as in :class:`HyperParamSetter`.
          file_name(str): a file containing the new value of the parameter.
              Each line in the file is a ``k:v`` pair, for example, ``learning_rate:1e-4``.
              If the pair is not found, the param will not be changed.
      """
      super(HumanHyperParamSetter, self).__init__(param)
      self.file_name = os.path.join(logger.get_logger_dir(), file_name)
      logger.info(
          "Use {} to set hyperparam: '{}'.".format(
              self.file_name, self.param.readable_name
          )
      )
  
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 1 to create a new HumanHyperParamSetter object from inherited base classes: HyperParamSetter with example: obj = HumanHyperParamSetter(param, file_name)