class_docstr: Customizes the behavior of a particular parameter.
docstr: Construct a parameter modifier that may be specific to a parameter.
  
  Args:
    parameter_name:  A `ParameterModifier` instance may operate on a class of
      parameters or on a parameter with a particular name.  Only
      `ParameterModifier` instances that are of a unique type or were
      initialized with a unique `parameter_name` will be executed.
      See `__eq__` and `__hash__`.
docstr_tok: ["Construct","a","parameter","modifier","that","may","be","specific","to","a","parameter",".","Args",":","parameter_name",":","A","`","ParameterModifier","`","instance","may","operate","on","a","class","of","parameters","or","on","a","parameter","with","a","particular","name",".","Only","`","ParameterModifier","`","instances","that","are","of","a","unique","type","or","were","initialized","with","a","unique","`","parameter_name","`","will","be","executed",".","See","`","__eq__","`","and","`","__hash__","`","."]
code: def __init__(self, parameter_name=None):
      """Construct a parameter modifier that may be specific to a parameter.
  
      Args:
        parameter_name:  A `ParameterModifier` instance may operate on a class of
          parameters or on a parameter with a particular name.  Only
          `ParameterModifier` instances that are of a unique type or were
          initialized with a unique `parameter_name` will be executed.
          See `__eq__` and `__hash__`.
      """
      object.__init__(self)
      self._parameter_name = parameter_name
  
imports: from collections import OrderedDict
  import contextlib
  import types
  import unittest
  from absl.testing import parameterized
  import six
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 1 to create a new ParameterModifier object from inherited base classes: object with example: obj = ParameterModifier(parameter_name)