original_docstring: 
      Create an optimizer for the given parameters as specified in config.
  
      Except for the weight decay and initial learning rate,
      default optimizer settings are used.
  
      Currently supported configuration settings for "optimizer":
          - "sgd" (default): see `torch.optim.SGD`
          - "adam": see `torch.optim.adam`
          - "adagrad": see `torch.optim.adagrad`
          - "adadelta": see `torch.optim.adadelta`
          - "rmsprop": see `torch.optim.RMSprop`
  
      The initial learning rate is set according to "learning_rate" in the config.
      The weight decay is set according to "weight_decay" in the config.
      If they are not specified, the initial learning rate is set to 3.0e-4, the
      weight decay to 0.
  
      Note that the scheduler state is saved in the checkpoint, so if you load
      a model for further training you have to use the same type of scheduler.
  
      :param config: configuration dictionary
      :param parameters:
      :return: optimizer
      
docstring: Create an optimizer for the given parameters as specified in config.
  Except for the weight decay and initial learning rate,
  default optimizer settings are used.
  
  
  
  The initial learning rate is set according to "learning_rate" in the config.
  The weight decay is set according to "weight_decay" in the config.
  If they are not specified, the initial learning rate is set to 3.0e-4, the
  weight decay to 0.
  
  Note that the scheduler state is saved in the checkpoint, so if you load
  a model for further training you have to use the same type of scheduler.
docstring_tokens: Create an optimizer for the given parameters as specified in config . Except for the weight decay and initial learning rate default optimizer settings are used . The initial learning rate is set according to " learning_rate " in the config . The weight decay is set according to " weight_decay " in the config . If they are not specified the initial learning rate is set to 3 . 0e - 4 the weight decay to 0 . Note that the scheduler state is saved in the checkpoint so if you load a model for further training you have to use the same type of scheduler .
code: def build_optimizer(config: dict, parameters: Generator) -> Optimizer:
      """
      Create an optimizer for the given parameters as specified in config.
  
      Except for the weight decay and initial learning rate,
      default optimizer settings are used.
  
      Currently supported configuration settings for "optimizer":
          - "sgd" (default): see `torch.optim.SGD`
          - "adam": see `torch.optim.adam`
          - "adagrad": see `torch.optim.adagrad`
          - "adadelta": see `torch.optim.adadelta`
          - "rmsprop": see `torch.optim.RMSprop`
  
      The initial learning rate is set according to "learning_rate" in the config.
      The weight decay is set according to "weight_decay" in the config.
      If they are not specified, the initial learning rate is set to 3.0e-4, the
      weight decay to 0.
  
      Note that the scheduler state is saved in the checkpoint, so if you load
      a model for further training you have to use the same type of scheduler.
  
      :param config: configuration dictionary
      :param parameters:
      :return: optimizer
      """
      optimizer_name = config.get("optimizer", "sgd").lower()
      learning_rate = config.get("learning_rate", 3.0e-4)
      weight_decay = config.get("weight_decay", 0)
  
      if optimizer_name == "adam":
          adam_betas = config.get("adam_betas", (0.9, 0.999))
          optimizer = torch.optim.Adam(parameters,
                                       weight_decay=weight_decay,
                                       lr=learning_rate,
                                       betas=adam_betas)
      elif optimizer_name == "adagrad":
          optimizer = torch.optim.Adagrad(parameters,
                                          weight_decay=weight_decay,
                                          lr=learning_rate)
      elif optimizer_name == "adadelta":
          optimizer = torch.optim.Adadelta(parameters,
                                           weight_decay=weight_decay,
                                           lr=learning_rate)
      elif optimizer_name == "rmsprop":
          optimizer = torch.optim.RMSprop(parameters,
                                          weight_decay=weight_decay,
                                          lr=learning_rate)
      elif optimizer_name == "sgd":
          # default
          optimizer = torch.optim.SGD(parameters,
                                      weight_decay=weight_decay,
                                      lr=learning_rate)
      else:
          raise ConfigurationError("Invalid optimizer. Valid options: 'adam', "
                                   "'adagrad', 'adadelta', 'rmsprop', 'sgd'.")
      return optimizer
code_tokens: def build_optimizer ( config : dict , parameters : Generator ) -> Optimizer : optimizer_name = config . get ( "optimizer" , "sgd" ) . lower ( ) learning_rate = config . get ( "learning_rate" , 3.0e-4 ) weight_decay = config . get ( "weight_decay" , 0 ) if optimizer_name == "adam" : adam_betas = config . get ( "adam_betas" , ( 0.9 , 0.999 ) ) optimizer = torch . optim . Adam ( parameters , weight_decay = weight_decay , lr = learning_rate , betas = adam_betas ) elif optimizer_name == "adagrad" : optimizer = torch . optim . Adagrad ( parameters , weight_decay = weight_decay , lr = learning_rate ) elif optimizer_name == "adadelta" : optimizer = torch . optim . Adadelta ( parameters , weight_decay = weight_decay , lr = learning_rate ) elif optimizer_name == "rmsprop" : optimizer = torch . optim . RMSprop ( parameters , weight_decay = weight_decay , lr = learning_rate ) elif optimizer_name == "sgd" : optimizer = torch . optim . SGD ( parameters , weight_decay = weight_decay , lr = learning_rate ) else : raise ConfigurationError ( "Invalid optimizer. Valid options: 'adam', " "'adagrad', 'adadelta', 'rmsprop', 'sgd'." ) return optimizer
short_docstring: Create an optimizer for the given parameters as specified in config.
short_docstring_tokens: Create an optimizer for the given parameters as specified in config .
comment: """
      Create an optimizer for the given parameters as specified in config.
  
      Except for the weight decay and initial learning rate,
      default optimizer settings are used.
  
      Currently supported configuration settings for "optimizer":
          - "sgd" (default): see `torch.optim.SGD`
          - "adam": see `torch.optim.adam`
          - "adagrad": see `torch.optim.adagrad`
          - "adadelta": see `torch.optim.adadelta`
          - "rmsprop": see `torch.optim.RMSprop`
  
      The initial learning rate is set according to "learning_rate" in the config.
      The weight decay is set according to "weight_decay" in the config.
      If they are not specified, the initial learning rate is set to 3.0e-4, the
      weight decay to 0.
  
      Note that the scheduler state is saved in the checkpoint, so if you load
      a model for further training you have to use the same type of scheduler.
  
      :param config: configuration dictionary
      :param parameters:
      :return: optimizer
      """$SEP$# default