original_docstring:  Initializes the hyperparameters needed by the algorithm.
          
          Parameters
          ----------
          hyperparam_dict : dictionary
              Consists of key value pairs. The Loss will check the keys to get the corresponding params
              
              - **margin** - Margin to be used in adversarial loss computation (default:3)
              
              - **alpha** - Temperature of sampling (default:0.5)
          
docstring: Initializes the hyperparameters needed by the algorithm.
  Parameters
  
  hyperparam_dict : dictionary
  Consists of key value pairs. The Loss will check the keys to get the corresponding params
  
  margin** - Margin to be used in adversarial loss computation (default:3)
  
  alpha** - Temperature of sampling (default:0.5)
docstring_tokens: Initializes the hyperparameters needed by the algorithm . Parameters hyperparam_dict : dictionary Consists of key value pairs . The Loss will check the keys to get the corresponding params margin ** - Margin to be used in adversarial loss computation ( default : 3 ) alpha ** - Temperature of sampling ( default : 0 . 5 )
code: def _init_hyperparams(self, hyperparam_dict):
          """ Initializes the hyperparameters needed by the algorithm.
          
          Parameters
          ----------
          hyperparam_dict : dictionary
              Consists of key value pairs. The Loss will check the keys to get the corresponding params
              
              - **margin** - Margin to be used in adversarial loss computation (default:3)
              
              - **alpha** - Temperature of sampling (default:0.5)
          """
          self._loss_parameters['margin'] = hyperparam_dict.get('margin', DEFAULT_MARGIN_ADVERSARIAL)
          self._loss_parameters['alpha'] = hyperparam_dict.get('alpha', DEFAULT_ALPHA_ADVERSARIAL)
code_tokens: def _init_hyperparams ( self , hyperparam_dict ) : self . _loss_parameters [ 'margin' ] = hyperparam_dict . get ( 'margin' , DEFAULT_MARGIN_ADVERSARIAL ) self . _loss_parameters [ 'alpha' ] = hyperparam_dict . get ( 'alpha' , DEFAULT_ALPHA_ADVERSARIAL )
short_docstring: Initializes the hyperparameters needed by the algorithm.
short_docstring_tokens: Initializes the hyperparameters needed by the algorithm .
comment: """ Initializes the hyperparameters needed by the algorithm.
          
          Parameters
          ----------
          hyperparam_dict : dictionary
              Consists of key value pairs. The Loss will check the keys to get the corresponding params
              
              - **margin** - Margin to be used in adversarial loss computation (default:3)
              
              - **alpha** - Temperature of sampling (default:0.5)
          """