class_docstr: DummyLayer to ensure that the gradient checkpointing will assign output layer as require_grad=True.
  Reference: https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117/9
code: def __init__(self):
      super().__init__()
      self.dummy_bias = torch.ones(
          1, dtype=torch.float32, requires_grad=True
      )
  
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 1 to create a new DummyLayer object from inherited base classes: nn with example: obj = DummyLayer()