class_docstr: Estimator implementation for CenterNet.
  
  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.
class_docstr_tok: ["Estimator","implementation","for","CenterNet",".","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","."]
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 not self.classes or not self.num_class:
          raise ValueError("Unable to determine classes of dataset")
      train_dataset = train_data.to_mxnet()
      val_dataset = val_data.to_mxnet()
  
      # dataloader
      batch_size = self._cfg.train.batch_size
      width, height = self._cfg.center_net.data_shape
      num_class = len(train_dataset.classes)
      batchify_fn = Tuple(
          [Stack() for _ in range(6)]
      )  # stack image, cls_targets, box_targets
      train_loader = gluon.data.DataLoader(
          train_dataset.transform(
              CenterNetDefaultTrainTransform(
                  width,
                  height,
                  num_class=num_class,
                  scale_factor=self.net.scale,
              )
          ),
          batch_size,
          True,
          batchify_fn=batchify_fn,
          last_batch="rollover",
          num_workers=self._cfg.train.num_workers,
      )
      val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
      val_loader = gluon.data.DataLoader(
          val_dataset.transform(
              CenterNetDefaultValTransform(width, height)
          ),
          self._cfg.valid.batch_size,
          False,
          batchify_fn=val_batchify_fn,
          last_batch="keep",
          num_workers=self._cfg.valid.num_workers,
      )
      train_eval_loader = gluon.data.DataLoader(
          train_dataset.transform(
              CenterNetDefaultValTransform(width, height)
          ),
          self._cfg.valid.batch_size,
          False,
          batchify_fn=val_batchify_fn,
          last_batch="keep",
          num_workers=self._cfg.valid.num_workers,
      )
      self._time_elapsed += time.time() - tic
      return self._train_loop(
          train_loader,
          val_loader,
          train_eval_loader,
          time_limit=time_limit,
      )
  
imports: import os
  import math
  import time
  import warnings
  from collections import OrderedDict
  from PIL import Image
  import pandas
  import numpy
  import mxnet
  from mxnet import autograd
  from mxnet import gluon
  from ..base_estimator import BaseEstimator, set_default
  from ....data.batchify import Tuple, Stack, Pad
  from ....data.transforms.presets.center_net import CenterNetDefaultTrainTransform
  from ....data.transforms.presets.center_net import CenterNetDefaultValTransform
  from ....data.transforms.presets.center_net import load_test, transform_test
  from ....loss import MaskedL1Loss, HeatmapFocalLoss
  from ....model_zoo import get_model
  from ....model_zoo.center_net import get_center_net, get_base_network
  from ....utils import LRScheduler, LRSequential
  from ....utils.metrics import VOCMApMetric, VOC07MApMetric
  from .default import CenterNetCfg
  from ...data.dataset import ObjectDetectionDataset
  from ..conf import _BEST_CHECKPOINT_FILE
  from ..utils import EarlyStopperOnPlateau
label_desc: Use image node_id 4 for calling the CenterNetEstimator obj's underlying member method code with example usage: obj._resume_fit(train_data, val_data, time_limit) and returns: self, unknown