code: def apply_sigmoid(output: Dict):
      """
      Apply the sigmoid to logits.
  
      Parameters
      ----------
      output
          The model output dict.
  
      Returns
      -------
      The output with logits transformed by sigmoid.
      """
      for k, v in output.items():
          output[k][LOGITS] = torch.sigmoid(v[LOGITS].float())
      return output
  
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 13 for calling a global function with example usage: apply_sigmoid(output) and returns: output