class_docstr: 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, TrajectoryTransformerModel):
          module.gradient_checkpointing = value
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import functional
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_trajectory_transformer import TrajectoryTransformerConfig
label_desc: Use image node_id 1 for calling the TrajectoryTransformerPreTrainedModel obj's underlying member method code with example usage: obj._set_gradient_checkpointing(module, value) without return types