docstr: Predict probabilities class probabilities rather than class labels.
  This is only for the classification. Calling it for regression 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.
  The output will always have shape (#samples, #classes, height, width).
code: def predict_proba(
      self,
      data: Union[pd.DataFrame, dict, list],
      as_pandas: Optional[bool] = None,
      as_multiclass: Optional[bool] = True,
      realtime: Optional[bool] = False,
      **kwargs,
  ):
      """
      Predict probabilities class probabilities rather than class labels.
      This is only for the classification. Calling it for regression 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.
      The output will always have shape (#samples, #classes, height, width).
      """
      assert (self._output_shape == 1 and as_multiclass == False) or (
          self._output_shape > 1 and as_multiclass == True
      )
      data = self.on_predict_start(data)
  
      outputs = self.predict_per_run(
          data=data,
          realtime=realtime,
          requires_label=False,
      )
  
      if as_multiclass:
          ret_type = SEMANTIC_MASK
      else:
          ret_type = LOGITS
  
      logits = self.post_process_prediction(data, outputs, ret_type)
  
      prob = []
      for logit in logits:
          logit = logit.numpy()
          if ret_type == SEMANTIC_MASK:
              prob.append(softmax(logit, axis=1))
          else:
              prob.append(logit)
  
      return prob
  
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 11 for calling the SemanticSegmentationLearner obj's underlying member method code with example usage: obj.predict_proba(data, as_pandas, as_multiclass, realtime) and returns: prob