code: def get_model_postprocess_fn(problem_type: str, loss_func: _Loss):
      """
      Get the postprocessing function for the model outputs.
  
      Parameters
      ----------
      problem_type
          The problem type, e.g., classification or regression.
      loss_func
          The loss function used in training.
  
      Returns
      -------
      The postprocessing function.
      """
      postprocess_func = None
      if problem_type == REGRESSION:
          if isinstance(loss_func, nn.BCEWithLogitsLoss):
              postprocess_func = apply_sigmoid
      elif problem_type == SEMANTIC_SEGMENTATION:
          if isinstance(loss_func, Mask2FormerLoss):
              postprocess_func = (
                  apply_multi_class_semantic_seg_postprocess
              )
          else:
              postprocess_func = apply_sigmoid
  
      return postprocess_func
  
imports: import logging
  import re
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from transformers import AutoConfig, AutoModel, AutoTokenizer, BertTokenizer, CLIPTokenizer, ElectraTokenizer
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerLoss
  from ..constants import AUTOMM, CLASS_LOGITS, COLUMN_FEATURES, FEATURES, LOGITS, MASKS, OCR, PEFT_ADDITIVE_STRATEGIES, REGRESSION, SEMANTIC_MASK, SEMANTIC_SEGMENTATION
  from .adaptation_layers import IA3Linear, IA3LoRALinear, LoRALinear
label_desc: Use image node_id 15 for calling a global function with example usage: get_model_postprocess_fn(problem_type, loss_func) and returns: postprocess_func