docstr: Generates a correct description.
  
  Arguments:
    standard_deviation: An integer standard deviation value.
  
  Returns:
    The correct description given a standard deviation value.
docstr_tok: ["Generates","a","correct","description",".","Arguments",":","standard_deviation",":","An","integer","standard","deviation","value",".","Returns",":","The","correct","description","given","a","standard","deviation","value","."]
code: def computeCorrectDescription(self, standard_deviation):
      """Generates a correct description.
  
      Arguments:
        standard_deviation: An integer standard deviation value.
  
      Returns:
        The correct description given a standard deviation value.
      """
      description = (
          "<p>The probabilities used to create this PR curve are "
          "generated from a normal distribution. Its standard "
          "deviation is initially %d and decreases"
          " over time.</p>"
      ) % standard_deviation
      return description
  
imports: import collections.abc
  import functools
  import os.path
  import numpy
  import tensorflow
  from tensorboard import context
  from tensorboard.backend.event_processing import plugin_event_multiplexer
  from tensorboard.backend.event_processing import data_provider
  from tensorboard.plugins import base_plugin
  from tensorboard.plugins.pr_curve import pr_curve_demo
  from tensorboard.plugins.pr_curve import pr_curves_plugin
label_desc: Use image node_id 3 for calling the PrCurvesPluginTest obj's underlying member method code with example usage: obj.computeCorrectDescription(standard_deviation) and returns: description