docstr: Augments the mixer's fit with new data, nr of epochs is based on the amount of epochs the original fitting took
  
  :param train_data: The network is fit/trained on this
  :param dev_data: Data used for early stopping and hyperparameter determination
code: def partial_fit(
      self,
      train_data: EncodedDs,
      dev_data: EncodedDs,
      args: Optional[dict] = None,
  ) -> None:
      """
      Augments the mixer's fit with new data, nr of epochs is based on the amount of epochs the original fitting took
  
      :param train_data: The network is fit/trained on this
      :param dev_data: Data used for early stopping and hyperparameter determination
      """
  
      # Based this on how long the initial training loop took, at a low learning rate as to not mock anything up tooo badly # noqa
      self.started = time.time()
      train_dl = DataLoader(
          train_data, batch_size=self.batch_size, shuffle=True
      )
      dev_dl = DataLoader(
          dev_data, batch_size=self.batch_size, shuffle=True
      )
      optimizer = self._select_optimizer(self.model, lr=self.lr)
      criterion = self._select_criterion()
      scaler = GradScaler()
  
      self.model, _, _ = self._max_fit(
          train_dl,
          dev_dl,
          criterion,
          optimizer,
          scaler,
          self.stop_after * 0.1,
          max(1, int(self.epochs_to_best / 3)),
      )
  
imports: import time
  from copy import deepcopy
  from collections import deque
  from typing import Dict, List, Optional
  import torch
  import numpy
  import pandas
  from torch import nn
  import torch_optimizer
  from sklearn.metrics import r2_score
  from torch.cuda.amp import GradScaler
  from torch.utils.data import DataLoader
  from torch.nn.modules.loss import MSELoss
  from torch.optim.optimizer import Optimizer
  from type_infer.dtype import dtype
  from lightwood.helpers.log import log
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.torch import LightwoodAutocast
  from lightwood.data.encoded_ds import EncodedDs
  from lightwood.mixer.base import BaseMixer
  from lightwood.mixer.helpers.ar_net import ArNet
  from lightwood.mixer.helpers.default_net import DefaultNet
  from lightwood.api.types import PredictionArguments
  from lightwood.mixer.helpers.transform_corss_entropy_loss import TransformCrossEntropyLoss
label_desc: Use image node_id 12 for calling the Neural obj's underlying member method code with example usage: obj.partial_fit(train_data, dev_data, args) without return types