class_docstr: Online naive finetuning.
  
  The simplest (and least effective) Continual Learning strategy. Naive just
  incrementally fine tunes a single model without employing any method
  to contrast the catastrophic forgetting of previous knowledge.
  This strategy does not use task identities.
  
  Naive is easy to set up and its results are commonly used to show the worst
  performing baseline.
docstr: Creates an instance of the Naive strategy.
  
  :param model: The model.
  :param optimizer: The optimizer to use.
  :param criterion: The loss criterion to use.
  :param num_passes: The number of passes for each sub-experience.
      Defaults to 1.
  :param train_mb_size: The train minibatch size. Defaults to 1.
  :param eval_mb_size: The eval minibatch size. Defaults to 1.
  :param device: The device to use. Defaults to None (cpu).
  :param plugins: Plugins to be added. Defaults to None.
  :param evaluator: (optional) instance of EvaluationPlugin for logging
      and metric computations.
  :param eval_every: the frequency of the calls to `eval` inside the
      training loop. -1 disables the evaluation. 0 means `eval` is called
      only at the end of the learning experience. Values >0 mean that
      `eval` is called every `eval_every` epochs and at the end of the
      learning experience.
code: def __init__(
      self,
      model: Module,
      optimizer: Optimizer,
      criterion=CrossEntropyLoss(),
      train_passes: int = 1,
      train_mb_size: int = 1,
      eval_mb_size: Optional[int] = None,
      device: Union[str, torch.device] = "cpu",
      plugins: Optional[Sequence[BasePlugin]] = None,
      evaluator: Union[
          EvaluationPlugin, Callable[[], EvaluationPlugin]
      ] = default_evaluator,
      eval_every=-1,
      **kwargs,
  ):
      """
      Creates an instance of the Naive strategy.
  
      :param model: The model.
      :param optimizer: The optimizer to use.
      :param criterion: The loss criterion to use.
      :param num_passes: The number of passes for each sub-experience.
          Defaults to 1.
      :param train_mb_size: The train minibatch size. Defaults to 1.
      :param eval_mb_size: The eval minibatch size. Defaults to 1.
      :param device: The device to use. Defaults to None (cpu).
      :param plugins: Plugins to be added. Defaults to None.
      :param evaluator: (optional) instance of EvaluationPlugin for logging
          and metric computations.
      :param eval_every: the frequency of the calls to `eval` inside the
          training loop. -1 disables the evaluation. 0 means `eval` is called
          only at the end of the learning experience. Values >0 mean that
          `eval` is called every `eval_every` epochs and at the end of the
          learning experience.
      """
      super().__init__(
          model=model,
          optimizer=optimizer,
          criterion=criterion,
          train_epochs=train_passes,
          train_mb_size=train_mb_size,
          eval_mb_size=eval_mb_size,
          device=device,
          plugins=plugins,
          evaluator=evaluator,
          eval_every=eval_every,
          **kwargs,
      )
  
file_path: avalanche/avalanche/training/supervised/strategy_wrappers_online.py
imports: from typing import Callable, Optional, Sequence, Union
  import torch
  from torch.nn import Module, CrossEntropyLoss
  from torch.optim import Optimizer
  from avalanche.core import BasePlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.plugins import EvaluationPlugin
  from avalanche.training.templates import SupervisedTemplate
  from avalanche._annotations import deprecated
label_desc: Use image node_id 1 to create a new OnlineNaive object from inherited base classes: SupervisedTemplate with example: obj = OnlineNaive(model, optimizer, criterion, train_passes, train_mb_size, eval_mb_size, device, plugins, evaluator, eval_every)