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.
      """
      self.on_predict_start()
      data = data_to_df(data=data)
      features = self.extract_embedding(data)
      pred = self._svm.predict(features)
      assert (
          self._label_column in data.columns
      ), f"Label {self._label_column} is not in the data. Cannot perform evaluation without ground truth labels."
      y_true = np.array(data[self._label_column])
      metric_data = {Y_PRED: pred, Y_TRUE: y_true}
      if metrics is None:
          if self._eval_metric_func:
              metrics = [self._eval_metric_func]
          else:
              metrics = [self._eval_metric_name]
      if isinstance(metrics, str) or isinstance(metrics, Scorer):
          metrics = [metrics]
  
      results = {}
      for per_metric in metrics:
          score = compute_score(
              metric_data=metric_data,
              metric=per_metric.lower()
              if isinstance(per_metric, str)
              else per_metric,
          )
          per_metric_name = (
              per_metric
              if isinstance(per_metric, str)
              else per_metric.name
          )
          results[per_metric_name] = score
  
      if return_pred:
          return results, self._as_pandas(
              data=data, to_be_converted=pred
          )
      return results
  
imports: import logging
  import os
  import pickle
  from datetime import timedelta
  from typing import Dict, List, Optional, Union
  import lightning.pytorch
  import numpy
  import pandas
  import torch
  from omegaconf import DictConfig
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from torch import nn
  from autogluon.core.metrics import Scorer
  from autogluon.core.utils.loaders import load_pd
  from ..constants import CLIP, COLUMN_FEATURES, HF_TEXT, TIMM_IMAGE, Y_PRED, Y_TRUE
  from ..data import BaseDataModule, MultiModalFeaturePreprocessor
  from ..utils import CustomUnpickler, LogFilter, apply_log_filter, compute_score, data_to_df, extract_from_output, get_available_devices, logits_to_prob, select_model, turn_on_off_feature_column_info
  from .base import BaseLearner
label_desc: Use image node_id 18 for calling the FewShotSVMLearner obj's underlying member method code with example usage: obj.evaluate(data, metrics, return_pred, realtime) and returns: results, results,  self