docstr: sample: dict
      Single data sample.
docstr_tok: ["sample",":","dict","Single","data","sample","."]
code: def evaluate(self, sample, stage=None):
      """
      sample: dict
          Single data sample.
      """
      pred_results = self._predict_step(sample)
  
      preds, target = self._get_map_input(pred_results)
  
      # use MeanAveragePrecision, example code: https://github.com/Lightning-AI/metrics/blob/master/examples/detection_map.py
      self.validation_metric.update(preds, target)
  
      return pred_results
  
imports: import logging
  from typing import Callable, Optional, Union
  import lightning.pytorch
  import torchmetrics
  from lightning.pytorch.utilities import grad_norm
  from torch.nn.modules.loss import _Loss
  from torchmetrics.aggregation import BaseAggregator
  from ..constants import BBOX, IMAGE, LABEL
  from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_lr_scheduler, get_optimizer, remove_parameters_without_grad
label_desc: Use image node_id 6 for calling the MMDetLitModule obj's underlying member method code with example usage: obj.evaluate(sample, stage) and returns: pred_results