class_docstr: Helper class uses for constructing Python bytecode
class_docstr_tok: ["Helper","class","uses","for","constructing","Python","bytecode"]
docstr: Call the generated code function stored in fn_name
docstr_tok: ["Call","the","generated","code","function","stored","in","fn_name"]
code: def make_call_generated_code(self, fn_name: str) -> None:
      """Call the generated code function stored in fn_name"""
      self.extend_output(self.load_function_name(fn_name, True))
  
      graphargs = self.tx.output.graphargs
      for arg in graphargs:
          if arg.is_unspecialized:
              self.extend_output(
                  [
                      self.create_load_python_module(torch, True),
                      self.create_load_attr("as_tensor"),
                  ]
              )
              self.extend_output(arg.load(self))
              self.extend_output(create_call_function(1, False))
          else:
              self.extend_output(arg.load(self))
  
      self.extend_output(create_call_function(len(graphargs), False))
  
imports: import collections
  import dataclasses
  import re
  import sys
  import types
  from typing import Counter, Dict, List, Optional
  import torch.nn
  from .None import utils
  from .bytecode_transformation import create_call_function, create_dup_top, create_instruction, create_load_global, create_rot_n, Instruction
  from .exc import unimplemented
  from .source import AttrSource, Source
  from .utils import is_safe_constant, rot_n_helper
  from .variables.base import VariableTracker
  from .variables.nn_module import NNModuleVariable
  from .variables.tensor import NumpyNdarrayVariable, SymNodeVariable, TensorVariable, UnspecializedPythonVariable
  from .variables.torch_function import TensorWithTFOverrideVariable
label_desc: Use image node_id 27 for calling the PyCodegen obj's underlying member method code with example usage: obj.make_call_generated_code(fn_name) without return types