code: def codegen_stage(
      self,
      stage: List[Node],
      s_ix: int,
      op_to_node,
      code,
      already_defined,
  ):
      fun_name = f"stage_code_{s_ix}"
      base_code = ast.parse(
          f"""
  def {fun_name}(batch_indices, metadata, storage_state):
      pass
          """
      ).body[0]
  
      base_code.args.args.extend(
          [ast.arg(arg=f"memory_{x}") for x in code["operation"]]
      )
  
      base_code.args.args.extend(
          [ast.arg(arg=f"shared_memory_{x}") for x in code["shared"]]
      )
  
      base_code.args.args.extend(
          [ast.arg(f"result_{x}") for x in already_defined]
      )
  
      return_tuple = ast.Return(
          value=ast.Tuple(elts=[], ctx=ast.Load())
      )
  
      defined_here = []
  
      base_code.body.pop()
      compiled_functions = {}
      for node_id in stage:
          node: Node = self.id_to_node[node_id]
          has_shared_state = (
              node_id in code["shared"]
              and code["shared"][node_id] is not None
          )
  
          try:
              compiled_functions[f"code_{node_id}"] = code["operation"][
                  node_id
              ]
          except KeyError:
              pass  # No code for this node
  
          func_call_ast = node.func_call_ast
          if has_shared_state:
              fname = f"init_shared_state_code_{node_id}"
              compiled_functions[fname] = code["shared"][node_id]
              base_code.body.append(
                  node.get_shared_code_ast(op_to_node)
              )
              func_call_ast.value.args.extend(
                  [
                      ast.Name(
                          id=f"shared_memory_{op_to_node[node.operation]}",
                          ctx=ast.Load(),
                      ),
                  ]
              )
  
          base_code.body.append(func_call_ast)
          return_tuple.value.elts.append(
              ast.Name(id=node.result_id, ctx=ast.Load())
          )
          already_defined.append(node.id)
          defined_here.append(node.id)
  
      # If the stage is even we are compiling it
      if s_ix % 2 == 0:
          compiled_functions = {
              k: Compiler.compile(v)
              for (k, v) in compiled_functions.items()
          }
  
      base_code.body.append(return_tuple)
  
      module = ast.fix_missing_locations(
          ast.Module(body=[base_code], type_ignores=[])
      )
  
      # print(astor.to_source(base_code))
      namespace = {**compiled_functions}
  
      exec(compile(module, "", "exec"), namespace)
      final_code = namespace[fun_name]
      return final_code, defined_here
  
imports: from distutils.log import warn
  import warnings
  import ast
  from collections import defaultdict
  from typing import Callable, Dict, List, Optional, Sequence, Set
  from abc import ABC, abstractmethod
  from ffcv.pipeline.allocation_query import AllocationQuery
  from ffcv.pipeline.pipeline_spec import PipelineSpec
  from ffcv.pipeline.compiler import Compiler
  from ffcv.pipeline.allocation_query import allocate_query
  from .operation import Operation
  from ..transforms import ModuleWrapper
  from .state import State
  import torch
  import numpy
label_desc: Use image node_id 5 for calling the Graph obj's underlying member method code with example usage: obj.codegen_stage(stage, s_ix, op_to_node, code, already_defined) and returns: final_code,  defined_here