class_docstr: A variable in the graph (e.g. learning_rate) can be a hyperparam.
docstr: Args:
      name(str): name of the variable.
      shape(tuple): shape of the variable.
docstr_tok: ["Args",":","name","(","str",")",":","name","of","the","variable",".","shape","(","tuple",")",":","shape","of","the","variable","."]
code: def __init__(self, name, shape=()):
      """
      Args:
          name(str): name of the variable.
          shape(tuple): shape of the variable.
      """
      self.name = name
      self.shape = shape
      self._readable_name, self.var_name = get_op_tensor_name(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 GraphVarParam object from inherited base classes: HyperParam with example: obj = GraphVarParam(name, shape)