docstr: Post-process segmentation results to match the size of original input images.
  
  Parameters
  ----------
  data
      Pandas data frame or dict containing the image information.
  outputs
      A list of segmentation output results.
  ret_type
      What kind of information to extract from model outputs.
  
  Returns
  -------
  A list of the post-processed segmentation results.
code: def post_process_prediction(self, data, outputs, ret_type):
      """
      Post-process segmentation results to match the size of original input images.
  
      Parameters
      ----------
      data
          Pandas data frame or dict containing the image information.
      outputs
          A list of segmentation output results.
      ret_type
          What kind of information to extract from model outputs.
  
      Returns
      -------
      A list of the post-processed segmentation results.
      """
      logits = [ele[ret_type] for ele in outputs]
      image_column_name = self.get_image_column_name(data)
      for idx in range(data.shape[0]):
          ori_image_size = Image.open(
              data[image_column_name][idx]
          ).size  # width, height
          logits[idx] = F.interpolate(
              logits[idx].float(),
              (ori_image_size[1], ori_image_size[0]),
              mode="bilinear",
              align_corners=False,
          )
      return logits
  
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 14 for calling the SemanticSegmentationLearner obj's underlying member method code with example usage: obj.post_process_prediction(data, outputs, ret_type) and returns: logits