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, GPTNeoXModel):
          module.gradient_checkpointing = value
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_gpt_neox import GPTNeoXConfig
label_desc: Use image node_id 2 for calling the GPTNeoXPreTrainedModel obj's underlying member method code with example usage: obj._set_gradient_checkpointing(module, value) without return types