code: def _set_gradient_checkpointing(self, module, value=False):
      if isinstance(module, BertEncoder):
          module.gradient_checkpointing = value
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from ...modeling_outputs import BaseModelOutputWithPooling
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..bert.modeling_bert import BertEncoder, BertModel
  from .configuration_dpr import DPRConfig
label_desc: Use image node_id 2 for calling the DPRPreTrainedModel obj's underlying member method code with example usage: obj._set_gradient_checkpointing(module, value) without return types