class_docstr: Context-manager that enables the experimental mode to test new but
  potentially unstable features.
  
  .. code-block:: python
  
      with torch_geometric.experimental_mode():
          out = model(data.x, data.edge_index)
  
  Args:
      options (str or list, optional): Currently there are no experimental
          features.
class_docstr_tok: ["Context-manager","that","enables","the","experimental","mode","to","test","new","but","potentially","unstable","features",".","..","code-block",":",":","python","with","torch_geometric.experimental_mode","(",")",":","out","=","model","(","data.x",",","data.edge_index",")","Args",":","options","(","str","or","list",",","optional",")",":","Currently","there","are","no","experimental","features","."]
code: def __init__(self, options: Options = None) -> None:
      self.options = get_options(options)
      self.previous_state = {
          option: __experimental_flag__[option]
          for option in self.options
      }
  
file_path: pytorch_geometric/torch_geometric/experimental.py
imports: import functools
  import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from torch_geometric.utils import *
label_desc: Use image node_id 1 to create a new experimental_mode object  with example: obj = experimental_mode(options)