class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
code: def _set_gradient_checkpointing(self, module, value=False):
      if isinstance(module, DecisionTransformerGPT2Model):
          module.gradient_checkpointing = value
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.cuda.amp import autocast
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_decision_transformer import DecisionTransformerConfig
label_desc: Use image node_id 3 for calling the DecisionTransformerGPT2PreTrainedModel obj's underlying member method code with example usage: obj._set_gradient_checkpointing(module, value) without return types