docstr: Predict probabilities class probabilities rather than class labels.
  This is only for the classification tasks. Calling it for a regression task will throw an exception.
  
  Parameters
  ----------
  data
      The data to make predictions for. Should contain same column names as training data and
        follow same format (except for the `label` column).
  as_pandas
      Whether to return the output as a pandas DataFrame(Series) (True) or numpy array (False).
  as_multiclass
      Whether to return the probability of all labels or
      just return the probability of the positive class for binary classification problems.
  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
  -------
  Array of predicted class-probabilities, corresponding to each row in the given data.
  When as_multiclass is True, the output will always have shape (#samples, #classes).
  Otherwise, the output will have shape (#samples,)
code: def predict_proba(
      self,
      data,
      as_pandas: Optional[bool] = False,
      as_multiclass: Optional[bool] = True,
      realtime: Optional[bool] = False,
      **kwargs,
  ):
      """
      Predict probabilities class probabilities rather than class labels.
      This is only for the classification tasks. Calling it for a regression task will throw an exception.
  
      Parameters
      ----------
      data
          The data to make predictions for. Should contain same column names as training data and
            follow same format (except for the `label` column).
      as_pandas
          Whether to return the output as a pandas DataFrame(Series) (True) or numpy array (False).
      as_multiclass
          Whether to return the probability of all labels or
          just return the probability of the positive class for binary classification problems.
      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
      -------
      Array of predicted class-probabilities, corresponding to each row in the given data.
      When as_multiclass is True, the output will always have shape (#samples, #classes).
      Otherwise, the output will have shape (#samples,)
      """
      self.on_predict_start()
      features = self.extract_embedding(data, realtime=realtime)
      logits = self._svm.decision_function(features)
      prob = logits_to_prob(logits)
      if (
          as_pandas is None and isinstance(data, pd.DataFrame)
      ) or as_pandas is True:
          prob = self._as_pandas(data=data, to_be_converted=prob)
      return prob
  
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 16 for calling the FewShotSVMLearner obj's underlying member method code with example usage: obj.predict_proba(data, as_pandas, as_multiclass, realtime) and returns: prob