docstr: Rounds a categorical variable by setting to one the max of the given vector and to zero the rest of the entries.
  Assumes an 1x[number of categories] array (due to one-hot encoding) as an input
docstr_tok: ["Rounds","a","categorical","variable","by","setting","to","one","the","max","of","the","given","vector","and","to","zero","the","rest","of","the","entries",".","Assumes","an","1x","[","number","of","categories","]","array","(","due","to","one-hot","encoding",")","as","an","input"]
code: def round(self, value_array):
      """
      Rounds a categorical variable by setting to one the max of the given vector and to zero the rest of the entries.
      Assumes an 1x[number of categories] array (due to one-hot encoding) as an input
      """
  
      rounded_values = np.zeros(value_array.shape)
      rounded_values[np.argmax(value_array)] = 1
      return rounded_values
  
imports: import numpy
  from copy import deepcopy
  from ..errors import InvalidConfigError
label_desc: Use image node_id 8 for calling the CategoricalVariable obj's underlying member method code with example usage: obj.round(value_array) and returns: rounded_values