class_docstr: VariableTracker.mutable_local marker to indicate a list passed as
  an input that if we mutate we need to re-apply those mutations after
  the graph runs.
class_docstr_tok: ["VariableTracker.mutable_local","marker","to","indicate","a","list","passed","as","an","input","that","if","we","mutate","we","need","to","re-apply","those","mutations","after","the","graph","runs","."]
code: def __init__(self, source: Source, is_modified: bool = False):
      super().__init__(MutableLocalSource.Existing)
      self.source = source
      self.is_modified = is_modified
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch.nn
  from .None import utils, variables
  from .bytecode_transformation import create_call_function, create_call_method, create_instruction
  from .codegen import PyCodegen
  from .exc import unimplemented
  from .source import LocalSource, Source
  from .utils import nn_module_new, object_new
  from .variables.base import is_side_effect_safe, MutableLocalBase, MutableLocalSource, VariableTracker
label_desc: Use image node_id 1 to create a new MutableSideEffects object from inherited base classes: MutableLocalBase with example: obj = MutableSideEffects(source, is_modified)