class_docstr: Estimator implementation for Image Classification.
  
  Parameters
  ----------
  config : dict
      Config in nested dict.
  logger : logging.Logger
      Optional logger for this estimator, can be `None` when default setting is used.
  reporter : callable
      The reporter for metric checkpointing.
  net : mx.gluon.Block
      The custom network. If defined, the model name in config will be ignored so your
      custom network will be used for training rather than pulling it from model zoo.
class_docstr_tok: ["Estimator","implementation","for","Image","Classification",".","Parameters","--","--","--","--","--","config",":","dict","Config","in","nested","dict",".","logger",":","logging.Logger","Optional","logger","for","this","estimator",",","can","be","`","None","`","when","default","setting","is","used",".","reporter",":","callable","The","reporter","for","metric","checkpointing",".","net",":","mx.gluon.Block","The","custom","network",".","If","defined",",","the","model","name","in","config","will","be","ignored","so","your","custom","network","will","be","used","for","training","rather","than","pulling","it","from","model","zoo","."]
code: def _resume_fit(self, train_data, val_data, time_limit=math.inf):
      tic = time.time()
      if (
          max(self._cfg.train.start_epoch, self.epoch)
          >= self._cfg.train.epochs
      ):
          return {"time", self._time_elapsed}
      if self._problem_type != REGRESSION and (
          not self.classes or not self.num_class
      ):
          raise ValueError(
              "This is a classification problem and we are not able to determine classes of dataset"
          )
  
      num_workers = self._cfg.train.num_workers
      if self._cfg.train.use_rec:
          self._logger.info(
              f"Loading data from rec files: {self._cfg.train.rec_train}/{self._cfg.train.rec_val}"
          )
          train_loader, val_loader, self.batch_fn = get_data_rec(
              self._cfg.train.rec_train,
              self._cfg.train.rec_train_idx,
              self._cfg.train.rec_val,
              self._cfg.train.rec_val_idx,
              self.batch_size,
              num_workers,
              self.input_size,
              self._cfg.train.crop_ratio,
          )
      else:
          train_dataset = train_data.to_mxnet()
          val_dataset = val_data.to_mxnet()
          train_loader, val_loader, self.batch_fn = get_data_loader(
              self._cfg.train.data_dir,
              self.batch_size,
              num_workers,
              self.input_size,
              self._cfg.train.crop_ratio,
              train_dataset=train_dataset,
              val_dataset=val_dataset,
          )
      self._time_elapsed += time.time() - tic
      return self._train_loop(
          train_loader, val_loader, time_limit=time_limit
      )
  
imports: import time
  import os
  import math
  import copy
  from PIL import Image
  import pandas
  import numpy
  import mxnet
  from mxnet import gluon, nd
  from mxnet.optimizer import Optimizer
  from mxnet import autograd
  from mxnet.gluon.data.vision import transforms
  from ....data.transforms.presets.imagenet import transform_eval
  from ....model_zoo import get_model
  from ....utils import LRSequential, LRScheduler
  from ....None import nn
  from ....None import loss
  from ..base_estimator import BaseEstimator, set_default
  from ..constants import BINARY, MULTICLASS, REGRESSION
  from .utils import get_data_loader, get_data_rec, smooth, mixup_transform
  from .default import ImageClassificationCfg
  from ...data.dataset import ImageClassificationDataset
  from ..conf import _BEST_CHECKPOINT_FILE
  from ..utils import EarlyStopperOnPlateau
label_desc: Use image node_id 3 for calling the ImageClassificationEstimator obj's underlying member method code with example usage: obj._resume_fit(train_data, val_data, time_limit) and returns: self, unknown