code: def _set_gradient_checkpointing(self, module, value=False):
      if hasattr(module, "gradient_checkpointing"):
          module.gradient_checkpointing = value
  
imports: from dataclasses import dataclass
  from typing import Dict, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging
  from .attention_processor import Attention, AttentionProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 5 for calling the Kandinsky3UNet obj's underlying member method code with example usage: obj._set_gradient_checkpointing(module, value) without return types