docstr: Evaluate model on a test dataset.
  
  Parameters
  ----------
  data
      A dataframe, containing the same columns as the training data.
      Or a str, that is a path of the annotation file for detection.
  metrics
      A list of metric names to report.
      If None, we only return the score for the stored `_eval_metric_name`.
  return_pred
      Whether to return the prediction result of each row.
  realtime
      Whether to do realtime inference, which is efficient for small data (default False).
      If provided None, we would infer it on based on the data modalities
      and sample number.
  
  Returns
  -------
  A dictionary with the metric names and their corresponding scores.
  Optionally return a dataframe of prediction results.
code: def evaluate(
      self,
      data: Union[pd.DataFrame, dict, list, str],
      metrics: Optional[Union[str, List[str]]] = None,
      return_pred: Optional[bool] = False,
      realtime: Optional[bool] = False,
      **kwargs,
  ):
      """
      Evaluate model on a test dataset.
  
      Parameters
      ----------
      data
          A dataframe, containing the same columns as the training data.
          Or a str, that is a path of the annotation file for detection.
      metrics
          A list of metric names to report.
          If None, we only return the score for the stored `_eval_metric_name`.
      return_pred
          Whether to return the prediction result of each row.
      realtime
          Whether to do realtime inference, which is efficient for small data (default False).
          If provided None, we would infer it on based on the data modalities
          and sample number.
  
      Returns
      -------
      A dictionary with the metric names and their corresponding scores.
      Optionally return a dataframe of prediction results.
      """
      data = self.on_predict_start(data)
      return self.evaluate_semantic_segmentation(
          data, metrics, realtime
      )
  
imports: import logging
  import os
  from typing import Dict, Iterable, List, Optional, Union
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  import torch.nn.functional
  from omegaconf import OmegaConf
  from PIL import Image
  from scipy.special import softmax
  from autogluon.core.metrics import Scorer
  from ..constants import LABEL, LOGITS, SEMANTIC_MASK, SEMANTIC_SEGMENTATION, SEMANTIC_SEGMENTATION_IMG
  from ..optimization.lit_semantic_seg import SemanticSegmentationLitModule
  from ..optimization.semantic_seg_metrics import Balanced_Error_Rate_Pred
  from ..optimization.semantic_seg_metrics import Binary_IoU_Pred
  from ..optimization.semantic_seg_metrics import COD_METRICS_NAMES_Pred
  from ..optimization.semantic_seg_metrics import Multiclass_IoU_Pred
  from ..optimization.utils import get_loss_func, get_norm_layer_param_names, get_trainable_params_efficient_finetune
  from ..utils import extract_from_output, setup_save_path
  from .base import BaseLearner
  from ..constants import BER, EM, FM, IOU, MAE, SEMANTIC_SEGMENTATION, SM
label_desc: Use image node_id 9 for calling the SemanticSegmentationLearner obj's underlying member method code with example usage: obj.evaluate(data, metrics, return_pred, realtime) and returns: self